From e2872e676f042ddc118ffaded2cd57b663b03b52 Mon Sep 17 00:00:00 2001 From: Andrew Rosemberg Date: Sat, 14 Mar 2026 00:32:57 -0400 Subject: [PATCH 1/3] integrate multi-env mujoco-warp per-world geom_dataid feature --- .gitignore | 2 +- pyproject.toml | 2 +- scripts/demos/dex_manip.py | 174 + .../asset_zoo/robots/leap_hand/__init__.py | 3 + .../leap_hand/leap_left_custom_constants.py | 101 + .../asset_zoo/robots/leap_hand/xmls/LICENSE | 7 + .../leap_hand/xmls/assets/M2826_thumb_tip.obj | 31687 ++++++++++++++++ .../leap_hand/xmls/assets/M2826_tip.obj | 31687 ++++++++++++++++ .../leap_hand/xmls/left_hand_custom.xml | 393 + src/mjlab/envs/mdp/dr/__init__.py | 1 + src/mjlab/envs/mdp/dr/geom.py | 147 +- src/mjlab/scripts/train.py | 23 + src/mjlab/tasks/dex_manip/__init__.py | 15 + .../meshes/contactdb_objects/water_bottle.stl | Bin 0 -> 132384 bytes .../objects/meshes/ycb_objects/orange.stl | Bin 0 -> 503084 bytes .../meshes/ycb_objects/tuna_fish_can.stl | Bin 0 -> 480284 bytes src/mjlab/tasks/dex_manip/env_cfg.py | 516 + src/mjlab/tasks/dex_manip/mdp/__init__.py | 12 + src/mjlab/tasks/dex_manip/mdp/actions.py | 108 + src/mjlab/tasks/dex_manip/mdp/curriculums.py | 146 + src/mjlab/tasks/dex_manip/mdp/events.py | 156 + src/mjlab/tasks/dex_manip/mdp/numerics.py | 28 + src/mjlab/tasks/dex_manip/mdp/observations.py | 236 + src/mjlab/tasks/dex_manip/mdp/rewards.py | 243 + src/mjlab/tasks/dex_manip/mdp/terminations.py | 65 + src/mjlab/tasks/dex_manip/metrics.py | 238 + src/mjlab/tasks/dex_manip/objects.py | 69 + src/mjlab/tasks/dex_manip/rl_cfg.py | 42 + tests/test_dex_manip_task.py | 112 + tests/test_geom_dataid_randomization.py | 202 + uv.lock | 4 +- 31 files changed, 66414 insertions(+), 5 deletions(-) create mode 100644 scripts/demos/dex_manip.py create mode 100644 src/mjlab/asset_zoo/robots/leap_hand/__init__.py create mode 100644 src/mjlab/asset_zoo/robots/leap_hand/leap_left_custom_constants.py create mode 100644 src/mjlab/asset_zoo/robots/leap_hand/xmls/LICENSE create mode 100644 src/mjlab/asset_zoo/robots/leap_hand/xmls/assets/M2826_thumb_tip.obj create mode 100644 src/mjlab/asset_zoo/robots/leap_hand/xmls/assets/M2826_tip.obj create mode 100644 src/mjlab/asset_zoo/robots/leap_hand/xmls/left_hand_custom.xml create mode 100644 src/mjlab/tasks/dex_manip/__init__.py create mode 100644 src/mjlab/tasks/dex_manip/assets/objects/meshes/contactdb_objects/water_bottle.stl create mode 100644 src/mjlab/tasks/dex_manip/assets/objects/meshes/ycb_objects/orange.stl create mode 100644 src/mjlab/tasks/dex_manip/assets/objects/meshes/ycb_objects/tuna_fish_can.stl create mode 100644 src/mjlab/tasks/dex_manip/env_cfg.py create mode 100644 src/mjlab/tasks/dex_manip/mdp/__init__.py create mode 100644 src/mjlab/tasks/dex_manip/mdp/actions.py create mode 100644 src/mjlab/tasks/dex_manip/mdp/curriculums.py create mode 100644 src/mjlab/tasks/dex_manip/mdp/events.py create mode 100644 src/mjlab/tasks/dex_manip/mdp/numerics.py create mode 100644 src/mjlab/tasks/dex_manip/mdp/observations.py create mode 100644 src/mjlab/tasks/dex_manip/mdp/rewards.py create mode 100644 src/mjlab/tasks/dex_manip/mdp/terminations.py create mode 100644 src/mjlab/tasks/dex_manip/metrics.py create mode 100644 src/mjlab/tasks/dex_manip/objects.py create mode 100644 src/mjlab/tasks/dex_manip/rl_cfg.py create mode 100644 tests/test_dex_manip_task.py create mode 100644 tests/test_geom_dataid_randomization.py diff --git a/.gitignore b/.gitignore index 2559719f1..6aa942c59 100644 --- a/.gitignore +++ b/.gitignore @@ -13,8 +13,8 @@ artifacts/ .venv/ render_robots.py benchmark_results/ +*.sbatch # Documentation outputs. **/_build/* **/generated/* - diff --git a/pyproject.toml b/pyproject.toml index 5d2b1910e..5d9151122 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -134,7 +134,7 @@ torch = [ { index = "pytorch-cpu", extra = "cpu", marker = "sys_platform != 'darwin'" }, ] mujoco = { index = "mujoco" } -mujoco-warp = { git = "https://github.com/google-deepmind/mujoco_warp", rev = "d9bcc326544b845111eed21079bfa8f8738e2518" } +mujoco-warp = { git = "https://github.com/google-deepmind/mujoco_warp", rev = "e1b9d2640884801497ab0a3257145fd1e65d095b" } # PR #1191 head (per-world geom_dataid) [tool.ruff] src = ["src"] # Helpful for recognizing first-party imports. diff --git a/scripts/demos/dex_manip.py b/scripts/demos/dex_manip.py new file mode 100644 index 000000000..bb5fb6f13 --- /dev/null +++ b/scripts/demos/dex_manip.py @@ -0,0 +1,174 @@ +"""Record dexterous-manipulation demo videos across multiple object tasks. + +This script replays one trained policy checkpoint on `Mjlab-Dex-Manip` and +records one MP4 per requested object. + +Examples: + # Use latest checkpoint from a run directory and record all matching objects. + uv run python scripts/demos/dex_manip.py \ + --run-dir logs/rsl_rl/multi_object_left-custom/2026-03-01_12-00-00_run + + # Use an explicit checkpoint and a subset of objects. + uv run python scripts/demos/dex_manip.py \ + --checkpoint-file logs/rsl_rl/.../model_4000.pt \ + --objects water-bottle,orange,tuna-fish-can +""" + +from __future__ import annotations + +import re +from dataclasses import asdict, dataclass +from pathlib import Path + +import torch +import tyro + +import mjlab +from mjlab.envs import ManagerBasedRlEnv +from mjlab.rl import MjlabOnPolicyRunner, RslRlVecEnvWrapper +from mjlab.tasks.dex_manip.env_cfg import apply_dex_manip_overrides +from mjlab.tasks.dex_manip.objects import parse_object_selection +from mjlab.tasks.registry import load_env_cfg, load_rl_cfg, load_runner_cls +from mjlab.utils.torch import configure_torch_backends +from mjlab.utils.wrappers import VideoRecorder + + +@dataclass(frozen=True) +class DexManipDemoConfig: + checkpoint_file: str | None = None + run_dir: str | None = None + task_id: str = "Mjlab-Dex-Manip" + objects: str = "all" + """Comma/semicolon/space separated object names, or 'all'.""" + output_dir: str = "assets/demos/dex_manip" + steps: int = 400 + device: str | None = None + num_envs: int = 1 + video_height: int | None = 720 + video_width: int | None = 1280 + + +def _checkpoint_step(path: Path) -> int: + match = re.search(r"model_(\d+)\.pt$", path.name) + if match is None: + return -1 + return int(match.group(1)) + + +def _resolve_checkpoint(cfg: DexManipDemoConfig) -> Path: + if cfg.checkpoint_file is not None: + checkpoint = Path(cfg.checkpoint_file).expanduser().resolve() + if not checkpoint.is_file(): + raise FileNotFoundError(f"Checkpoint file not found: {checkpoint}") + return checkpoint + + if cfg.run_dir is None: + raise ValueError("Pass either --checkpoint-file or --run-dir.") + + run_dir = Path(cfg.run_dir).expanduser().resolve() + if not run_dir.is_dir(): + raise FileNotFoundError(f"Run dir not found: {run_dir}") + + candidates = [path for path in run_dir.glob("model_*.pt") if path.is_file()] + if not candidates: + raise FileNotFoundError(f"No model_*.pt checkpoints found under: {run_dir}") + + return max(candidates, key=lambda path: (_checkpoint_step(path), path.stat().st_mtime)) + + +def _resolve_objects(objects: str) -> list[str]: + return list(parse_object_selection(objects)) + + +def _record_one_task( + task_id: str, + object_name: str, + checkpoint: Path, + out_dir: Path, + device: str, + steps: int, + num_envs: int, + video_height: int | None, + video_width: int | None, +) -> Path: + env_cfg = load_env_cfg(task_id, play=True) + agent_cfg = load_rl_cfg(task_id) + + apply_dex_manip_overrides( + env_cfg, + objects=object_name, + envs_per_object=num_envs, + assignment_mode="cycle", + ) + + if video_height is not None: + env_cfg.viewer.height = video_height + if video_width is not None: + env_cfg.viewer.width = video_width + + name_prefix = f"{object_name}__{checkpoint.stem}" + expected_video_path = out_dir / f"{name_prefix}-step-0.mp4" + + env = ManagerBasedRlEnv(cfg=env_cfg, device=device, render_mode="rgb_array") + env = VideoRecorder( + env, + video_folder=out_dir, + step_trigger=lambda step: step == 0, + video_length=steps, + disable_logger=True, + name_prefix=name_prefix, + ) + env = RslRlVecEnvWrapper(env, clip_actions=agent_cfg.clip_actions) + + runner_cls = load_runner_cls(task_id) or MjlabOnPolicyRunner + runner = runner_cls(env, asdict(agent_cfg), device=device) + runner.load(str(checkpoint), load_cfg={"actor": True}, strict=True, map_location=device) + policy = runner.get_inference_policy(device=device) + policy.eval() + + obs, _ = env.reset() + with torch.no_grad(): + for _ in range(steps): + action = policy(obs) + obs, _, _, _ = env.step(action) + + env.close() + return expected_video_path + + +def main() -> None: + cfg = tyro.cli(DexManipDemoConfig, config=mjlab.TYRO_FLAGS) + configure_torch_backends() + + # Import tasks to populate the registry. + import mjlab.tasks as _mjlab_tasks # noqa: F401 + + device = cfg.device or ("cuda:0" if torch.cuda.is_available() else "cpu") + checkpoint = _resolve_checkpoint(cfg) + objects = _resolve_objects(cfg.objects) + out_dir = Path(cfg.output_dir).expanduser().resolve() + out_dir.mkdir(parents=True, exist_ok=True) + + print(f"[INFO] checkpoint={checkpoint}") + print(f"[INFO] device={device}") + print(f"[INFO] task_id={cfg.task_id}") + print(f"[INFO] objects={objects}") + print(f"[INFO] output_dir={out_dir}") + + for object_name in objects: + video_path = _record_one_task( + task_id=cfg.task_id, + object_name=object_name, + checkpoint=checkpoint, + out_dir=out_dir, + device=device, + steps=cfg.steps, + num_envs=cfg.num_envs, + video_height=cfg.video_height, + video_width=cfg.video_width, + ) + print(f"[DONE] {object_name} -> {video_path}") + + +if __name__ == "__main__": + main() diff --git a/src/mjlab/asset_zoo/robots/leap_hand/__init__.py b/src/mjlab/asset_zoo/robots/leap_hand/__init__.py new file mode 100644 index 000000000..69706117b --- /dev/null +++ b/src/mjlab/asset_zoo/robots/leap_hand/__init__.py @@ -0,0 +1,3 @@ +from mjlab.asset_zoo.robots.leap_hand.leap_left_custom_constants import ( + get_leap_left_custom_hand_cfg as get_leap_left_custom_hand_cfg, +) diff --git a/src/mjlab/asset_zoo/robots/leap_hand/leap_left_custom_constants.py b/src/mjlab/asset_zoo/robots/leap_hand/leap_left_custom_constants.py new file mode 100644 index 000000000..075bbf7e0 --- /dev/null +++ b/src/mjlab/asset_zoo/robots/leap_hand/leap_left_custom_constants.py @@ -0,0 +1,101 @@ +"""LEAP Hand (Left Custom) constants and EntityCfg for mjlab.""" + +from __future__ import annotations + +from pathlib import Path + +import mujoco + +from mjlab import MJLAB_SRC_PATH +from mjlab.actuator.xml_actuator import XmlPositionActuatorCfg +from mjlab.entity import EntityArticulationInfoCfg, EntityCfg +from mjlab.utils.os import update_assets + +HOME_KEYFRAME = EntityCfg.InitialStateCfg( + pos=(0.0, 0.0, 0.1), + joint_pos={ + "if_mcp": 0.131, + "if_rot": 0.0, + "if_pip": 0.65, + "if_dip": 1.0, + "mf_mcp": 0.131, + "mf_rot": 0.0, + "mf_pip": 0.65, + "mf_dip": 1.0, + "rf_mcp": 0.131, + "rf_rot": 0.0, + "rf_pip": 0.65, + "rf_dip": 1.0, + "th_cmc": 0.8, + "th_axl": -0.78, + "th_mcp": 0.5, + "th_ipl": 0.367, + }, + joint_vel={".*": 0.0}, +) + +LEAP_LEFT_CUSTOM_XML: Path = ( + MJLAB_SRC_PATH + / "asset_zoo" + / "robots" + / "leap_hand" + / "xmls" + / "left_hand_custom.xml" +) +assert LEAP_LEFT_CUSTOM_XML.exists(), f"Missing MJCF: {LEAP_LEFT_CUSTOM_XML}" + +LEAP_LEFT_CUSTOM_ACTUATION = EntityArticulationInfoCfg( + actuators=( + XmlPositionActuatorCfg(target_names_expr=("if_mcp",)), + XmlPositionActuatorCfg(target_names_expr=("if_rot",)), + XmlPositionActuatorCfg(target_names_expr=("if_pip",)), + XmlPositionActuatorCfg(target_names_expr=("if_dip",)), + XmlPositionActuatorCfg(target_names_expr=("mf_mcp",)), + XmlPositionActuatorCfg(target_names_expr=("mf_rot",)), + XmlPositionActuatorCfg(target_names_expr=("mf_pip",)), + XmlPositionActuatorCfg(target_names_expr=("mf_dip",)), + XmlPositionActuatorCfg(target_names_expr=("rf_mcp",)), + XmlPositionActuatorCfg(target_names_expr=("rf_rot",)), + XmlPositionActuatorCfg(target_names_expr=("rf_pip",)), + XmlPositionActuatorCfg(target_names_expr=("rf_dip",)), + XmlPositionActuatorCfg(target_names_expr=("th_cmc",)), + XmlPositionActuatorCfg(target_names_expr=("th_axl",)), + XmlPositionActuatorCfg(target_names_expr=("th_mcp",)), + XmlPositionActuatorCfg(target_names_expr=("th_ipl",)), + ), + soft_joint_pos_limit_factor=1.0, +) + + +def _get_assets(meshdir: str) -> dict[str, bytes]: + assets: dict[str, bytes] = {} + update_assets( + assets, + LEAP_LEFT_CUSTOM_XML.parent / "assets", + meshdir, + ) + return assets + + +def _get_spec() -> mujoco.MjSpec: + spec = mujoco.MjSpec.from_file(str(LEAP_LEFT_CUSTOM_XML)) + # Lift-style tasks consume a named end-effector site. + if spec.site("grasp_site") is None: + spec.body("palm").add_site( + name="grasp_site", + pos=(-0.125, -0.055, 0.02), + type=mujoco.mjtGeom.mjGEOM_SPHERE, + size=(0.005,), + rgba=(0.0, 1.0, 0.0, 0.0), + group=4, + ) + spec.assets = _get_assets(spec.meshdir) + return spec + + +def get_leap_left_custom_hand_cfg() -> EntityCfg: + return EntityCfg( + init_state=HOME_KEYFRAME, + spec_fn=_get_spec, + articulation=LEAP_LEFT_CUSTOM_ACTUATION, + ) diff --git a/src/mjlab/asset_zoo/robots/leap_hand/xmls/LICENSE b/src/mjlab/asset_zoo/robots/leap_hand/xmls/LICENSE new file mode 100644 index 000000000..8017908f3 --- /dev/null +++ b/src/mjlab/asset_zoo/robots/leap_hand/xmls/LICENSE @@ -0,0 +1,7 @@ +Copyright 2023 Ananye Agarwal + +Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. diff --git a/src/mjlab/asset_zoo/robots/leap_hand/xmls/assets/M2826_thumb_tip.obj b/src/mjlab/asset_zoo/robots/leap_hand/xmls/assets/M2826_thumb_tip.obj new file mode 100644 index 000000000..4d27c7c9d --- /dev/null +++ b/src/mjlab/asset_zoo/robots/leap_hand/xmls/assets/M2826_thumb_tip.obj @@ -0,0 +1,31687 @@ +# Blender 4.4.0 +# www.blender.org +mtllib Untitled.mtl +o M2826_combined +v 0.001124 -0.049978 -0.018650 +v 0.001703 -0.049978 -0.018650 +v 0.001703 -0.049928 -0.018700 +v 0.001103 -0.049928 -0.018700 +v 0.001703 -0.049928 -0.018480 +v 0.001703 -0.050278 -0.018480 +v 0.001703 -0.050278 -0.018650 +v 0.001553 -0.050278 -0.018650 +v 0.001553 -0.050278 -0.018480 +v 0.001553 -0.050106 -0.018650 +v 0.000996 -0.050106 -0.018650 +v 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a/src/mjlab/asset_zoo/robots/leap_hand/xmls/left_hand_custom.xml b/src/mjlab/asset_zoo/robots/leap_hand/xmls/left_hand_custom.xml new file mode 100644 index 000000000..d3aba758f --- /dev/null +++ b/src/mjlab/asset_zoo/robots/leap_hand/xmls/left_hand_custom.xml @@ -0,0 +1,393 @@ + + + + + diff --git a/src/mjlab/envs/mdp/dr/__init__.py b/src/mjlab/envs/mdp/dr/__init__.py index 2658e67a0..53fa05b3a 100644 --- a/src/mjlab/envs/mdp/dr/__init__.py +++ b/src/mjlab/envs/mdp/dr/__init__.py @@ -13,6 +13,7 @@ # Geom. # isort: split +from .geom import geom_dataid as geom_dataid from .geom import geom_friction as geom_friction from .geom import geom_pos as geom_pos from .geom import geom_quat as geom_quat diff --git a/src/mjlab/envs/mdp/dr/geom.py b/src/mjlab/envs/mdp/dr/geom.py index bf7b0835d..9deb062e5 100644 --- a/src/mjlab/envs/mdp/dr/geom.py +++ b/src/mjlab/envs/mdp/dr/geom.py @@ -2,7 +2,8 @@ from __future__ import annotations -from typing import TYPE_CHECKING +from collections.abc import Sequence +from typing import TYPE_CHECKING, Literal import mujoco import torch @@ -110,6 +111,55 @@ def _recompute_geom_bounds( env.sim.model.geom_aabb[env_grid, entity_grid, 1] = aabb_half +def _resolve_mesh_ids( + env: ManagerBasedRlEnv, mesh_ids: Sequence[int | str] +) -> torch.Tensor: + """Resolve mesh id candidates from integer ids or mesh names.""" + if len(mesh_ids) == 0: + raise ValueError("mesh_ids must contain at least one entry.") + + if all(isinstance(item, int) for item in mesh_ids): + ids = torch.as_tensor(mesh_ids, device=env.device, dtype=torch.long) + if torch.any(ids < -1): + raise ValueError("mesh_ids must be >= -1.") + return ids + + if all(isinstance(item, str) for item in mesh_ids): + mesh_names = [str(item) for item in mesh_ids] + available_names = [ + mesh.name for mesh in env.scene.spec.meshes if mesh.name is not None + ] + exact_name_to_id = {name: idx for idx, name in enumerate(available_names)} + + def _resolve_one(name: str) -> int: + # 1) Exact match first (e.g. "robot/m_small"). + if name in exact_name_to_id: + return exact_name_to_id[name] + + # 2) Fallback to basename match (e.g. "m_small"), requiring uniqueness. + suffix_matches = [ + idx for idx, full_name in enumerate(available_names) if full_name.rsplit("/", 1)[-1] == name + ] + if len(suffix_matches) == 1: + return suffix_matches[0] + if len(suffix_matches) > 1: + matched = [available_names[idx] for idx in suffix_matches] + raise ValueError( + f"Mesh name {name!r} is ambiguous. " + f"Matches: {matched}. Use fully qualified mesh names." + ) + raise ValueError( + f"Unknown mesh name {name!r}. Available: {sorted(available_names)}" + ) + + ids = [_resolve_one(name) for name in mesh_names] + return torch.as_tensor(ids, device=env.device, dtype=torch.long) + + raise TypeError( + "mesh_ids must be either all integers (mesh ids) or all strings (mesh names)." + ) + + # Per-field functions. @@ -283,3 +333,98 @@ def geom_size( default_axes=[0, 1, 2], ) _recompute_geom_bounds(env, env_ids, asset_cfg) + + +@requires_model_fields("geom_dataid") +def geom_dataid( + env: ManagerBasedRlEnv, + env_ids: torch.Tensor | None, + mesh_ids: Sequence[int | str], + asset_cfg: SceneEntityCfg = _DEFAULT_ASSET_CFG, + assignment_mode: Literal["sample", "cycle"] = "sample", + shared_random: bool = True, +) -> None: + """Assign per-world mesh/hfield ids for selected geoms. + + This enables true mixed-object simulation inside one batched environment when + ``sim.model.geom_dataid`` is per-world (shape ``(num_envs, ngeom)``), as provided + by newer MuJoCo-Warp builds. + + Args: + env: Environment instance. + env_ids: Environment indices to modify. ``None`` means all. + mesh_ids: Candidate mesh ids or mesh names. + asset_cfg: Entity and geom selection. + assignment_mode: + - ``"sample"``: sample candidate ids independently per environment. + - ``"cycle"``: deterministic round-robin assignment over ``mesh_ids``. + shared_random: If ``True``, all selected geoms in a world share one sampled id. + If ``False``, each selected geom is sampled independently. + """ + if env_ids is None: + env_ids = torch.arange(env.num_envs, device=env.device, dtype=torch.long) + else: + env_ids = env_ids.to(device=env.device, dtype=torch.long) + + mesh_id_candidates = _resolve_mesh_ids(env, mesh_ids) + if mesh_id_candidates.numel() == 0: + raise ValueError("mesh_ids resolved to an empty candidate set.") + + asset = env.scene[asset_cfg.name] + geom_indices = _get_entity_indices( + asset.indexing, asset_cfg, entity_type="geom", use_address=False + ).to(dtype=torch.long) + if geom_indices.numel() == 0: + raise ValueError("asset_cfg resolved to zero geoms for dr.geom_dataid.") + + # If the field has not been expanded by the event-manager bootstrap path, expand it + # once here so direct calls to dr.geom_dataid also get writable per-world storage. + if env.num_envs > 1 and "geom_dataid" not in env.sim.expanded_fields: + env.sim.expand_model_fields(("geom_dataid",)) + + model_field = env.sim.model.geom_dataid + if model_field.ndim != 2: + raise ValueError( + "dr.geom_dataid requires per-world geom_dataid storage with shape " + f"(num_envs, ngeom). Got shape={tuple(model_field.shape)}. " + "Install MuJoCo-Warp with per-world geom_dataid support (PR #1191 or newer)." + ) + if model_field.shape[0] != env.num_envs: + raise ValueError( + f"Expected geom_dataid.shape[0] == num_envs ({env.num_envs}), " + f"got {model_field.shape[0]}." + ) + if model_field.stride(0) == 0: + raise ValueError( + "geom_dataid appears to be a broadcasted view (stride(0)==0), so per-world " + "writes would alias across environments. Ensure geom_dataid is expanded via " + "sim.expand_model_fields(('geom_dataid',))." + ) + + n_envs = int(env_ids.numel()) + n_geoms = int(geom_indices.numel()) + n_choices = int(mesh_id_candidates.numel()) + + if assignment_mode == "sample": + if shared_random: + sample_idx = torch.randint(0, n_choices, (n_envs,), device=env.device) + chosen = mesh_id_candidates[sample_idx].unsqueeze(-1).expand(n_envs, n_geoms) + else: + sample_idx = torch.randint(0, n_choices, (n_envs, n_geoms), device=env.device) + chosen = mesh_id_candidates[sample_idx] + elif assignment_mode == "cycle": + cycle_idx = torch.arange(n_envs, device=env.device, dtype=torch.long) % n_choices + if shared_random: + chosen = mesh_id_candidates[cycle_idx].unsqueeze(-1).expand(n_envs, n_geoms) + else: + geom_offset = torch.arange(n_geoms, device=env.device, dtype=torch.long) + cycle_idx_2d = (cycle_idx.unsqueeze(-1) + geom_offset.unsqueeze(0)) % n_choices + chosen = mesh_id_candidates[cycle_idx_2d] + else: + raise ValueError( + f"Invalid assignment_mode={assignment_mode!r}. " + "Expected one of {'sample', 'cycle'}." + ) + + env_grid, geom_grid = torch.meshgrid(env_ids, geom_indices, indexing="ij") + model_field[env_grid, geom_grid] = chosen.to(dtype=model_field.dtype) diff --git a/src/mjlab/scripts/train.py b/src/mjlab/scripts/train.py index 3a204f112..6842f57dc 100644 --- a/src/mjlab/scripts/train.py +++ b/src/mjlab/scripts/train.py @@ -35,6 +35,12 @@ class TrainConfig: wandb_checkpoint_name: str | None = None """Optional checkpoint name within the W&B run to load (e.g. 'model_4000.pt').""" gpu_ids: list[int] | Literal["all"] | None = field(default_factory=lambda: [0]) + objects: str | None = None + """Dex-manip object selection. Use comma/semicolon/space separators, or 'all'.""" + envs_per_object: int | None = None + """Dex-manip only: number of parallel envs to allocate per selected object.""" + object_assignment_mode: Literal["cycle", "sample"] = "cycle" + """Dex-manip only: per-reset assignment strategy for selected objects.""" @staticmethod def from_task(task_id: str) -> "TrainConfig": @@ -44,6 +50,23 @@ def from_task(task_id: str) -> "TrainConfig": def run_train(task_id: str, cfg: TrainConfig, log_dir: Path) -> None: + if task_id == "Mjlab-Dex-Manip": + from mjlab.tasks.dex_manip.env_cfg import ( + apply_dex_manip_overrides, + ) + + selected = apply_dex_manip_overrides( + cfg.env, + objects=cfg.objects, + envs_per_object=cfg.envs_per_object, + assignment_mode=cfg.object_assignment_mode, + ) + print( + "[INFO] Dex manip overrides: " + f"objects={selected}, envs_per_object={cfg.envs_per_object}, " + f"num_envs={cfg.env.scene.num_envs}, assignment={cfg.object_assignment_mode}" + ) + cuda_visible = os.environ.get("CUDA_VISIBLE_DEVICES", "") if cuda_visible == "": device = "cpu" diff --git a/src/mjlab/tasks/dex_manip/__init__.py b/src/mjlab/tasks/dex_manip/__init__.py new file mode 100644 index 000000000..28d04c014 --- /dev/null +++ b/src/mjlab/tasks/dex_manip/__init__.py @@ -0,0 +1,15 @@ +from __future__ import annotations + +from mjlab.rl.runner import MjlabOnPolicyRunner +from mjlab.tasks.registry import register_mjlab_task + +from .env_cfg import dex_manip_env_cfg +from .rl_cfg import dex_manip_ppo_runner_cfg + +register_mjlab_task( + task_id="Mjlab-Dex-Manip", + env_cfg=dex_manip_env_cfg(), + play_env_cfg=dex_manip_env_cfg(play=True), + rl_cfg=dex_manip_ppo_runner_cfg(), + runner_cls=MjlabOnPolicyRunner, +) diff --git a/src/mjlab/tasks/dex_manip/assets/objects/meshes/contactdb_objects/water_bottle.stl b/src/mjlab/tasks/dex_manip/assets/objects/meshes/contactdb_objects/water_bottle.stl new file mode 100644 index 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zbD3TvveBF+*0p3y8JeeL`yiTm!%2(q2o()d9kNMc>h|-VWEMEtmUuAL z`!SiRUhL1U9t?XQ6X5u8DWDh3w8&W%NrmrGstk{DhNWHMgL-EvIQu&PPnyNy5{Z!f zK&l5rO6)hslrY8@E+OlZGdGeq%&>H5=%2azUjM|4clu+!Y%5tWzGwa5Z%`e#=-%a4 z=^Go5T2}jTMvzytd+715%Sn_9MQ mujoco.MjSpec: + mesh_names = object_names_to_mesh_names(DEFAULT_DEX_OBJECTS) + mesh_files = object_names_to_mesh_files(DEFAULT_DEX_OBJECTS) + mesh_xml = "\n".join( + f' ' + for mesh_name, mesh_file in zip(mesh_names, mesh_files, strict=True) + ) + xml = """ + + +__MESH_XML__ + + + + + + + + + """ + return mujoco.MjSpec.from_string(xml.replace("__MESH_XML__", mesh_xml)) + + +LEAP_ACTION_SCALE = 1.0 / 24.0 +PALM_CENTER_GEOM_EXPR = "palm_collision_.*" + + +def make_dex_manip_base_env_cfg() -> ManagerBasedRlEnvCfg: + actor_terms = { + "joint_pos": ObservationTermCfg( + func=dex_mdp.joint_pos_rel, + noise=Unoise(n_min=-0.01, n_max=0.01), + params={ + "asset_cfg": SceneEntityCfg("robot", joint_names=(".*",)), + "biased": True, + }, + ), + "prev_commanded_joint_pos": ObservationTermCfg( + func=dex_mdp.joint_pos_commanded, + params={ + "action_name": "joint_pos", + "asset_cfg": SceneEntityCfg("robot", joint_names=(".*",)), + }, + ), + } + + critic_terms = { + "joint_pos": ObservationTermCfg( + func=dex_mdp.joint_pos_rel, + params={"asset_cfg": SceneEntityCfg("robot", joint_names=(".*",))}, + ), + "joint_vel": ObservationTermCfg( + func=dex_mdp.joint_vel_rel, + params={"asset_cfg": SceneEntityCfg("robot", joint_names=(".*",))}, + ), + "joint_pos_error": ObservationTermCfg( + func=dex_mdp.joint_pos_command_error, + params={ + "action_name": "joint_pos", + "biased": True, + "asset_cfg": SceneEntityCfg("robot", joint_names=(".*",)), + }, + ), + "prev_commanded_joint_pos": ObservationTermCfg( + func=dex_mdp.joint_pos_commanded, + params={ + "action_name": "joint_pos", + "asset_cfg": SceneEntityCfg("robot", joint_names=(".*",)), + }, + ), + "object_pose_palm": ObservationTermCfg( + func=dex_mdp.object_pose_in_palm_frame, + params={ + "object_name": "object", + "hand_cfg": SceneEntityCfg("robot", body_names=("palm",)), + "palm_center_geom_expr": PALM_CENTER_GEOM_EXPR, + }, + ), + "object_lin_vel_palm": ObservationTermCfg( + func=dex_mdp.object_lin_vel_in_palm_frame, + params={ + "object_name": "object", + "hand_cfg": SceneEntityCfg("robot", body_names=("palm",)), + "palm_center_geom_expr": PALM_CENTER_GEOM_EXPR, + }, + ), + "object_ang_vel_palm": ObservationTermCfg( + func=dex_mdp.object_ang_vel_in_palm_frame, + params={ + "object_name": "object", + "hand_cfg": SceneEntityCfg("robot", body_names=("palm",)), + "palm_center_geom_expr": PALM_CENTER_GEOM_EXPR, + }, + ), + "object_size": ObservationTermCfg( + func=dex_mdp.object_size, + params={"object_name": "object", "geom_name": "object_geom"}, + ), + "object_mass": ObservationTermCfg( + func=dex_mdp.object_mass, + params={"object_name": "object", "body_name": "object"}, + ), + "object_com_offset_b": ObservationTermCfg( + func=dex_mdp.object_com_offset_b, + params={"object_name": "object", "body_name": "object"}, + ), + "object_friction": ObservationTermCfg( + func=dex_mdp.object_friction_coeff, + params={"object_name": "object", "geom_name": "object_geom", "axis": 0}, + ), + } + + observations = { + "actor": ObservationGroupCfg( + actor_terms, + enable_corruption=True, + history_length=10, + flatten_history_dim=True, + ), + "critic": ObservationGroupCfg( + critic_terms, + enable_corruption=False, + history_length=1, + flatten_history_dim=True, + ), + } + + actions: dict[str, ActionTermCfg] = { + "joint_pos": dex_mdp.JointPositionDeltaActionCfg( + entity_name="robot", + actuator_names=(".*",), + scale=1.0, + offset=0.0, + use_default_offset=False, + clip_to_joint_limits=True, + use_soft_joint_pos_limits=True, + delta_min=-LEAP_ACTION_SCALE, + delta_max=LEAP_ACTION_SCALE, + interpolate_decimation=True, + ) + } + + sensors = ( + ContactSensorCfg( + name="fingertip_object_contact", + primary=ContactMatch(mode="geom", pattern=".*_tip", entity="robot"), + secondary=ContactMatch(mode="geom", pattern="object_geom", entity="object"), + fields=("found",), + reduce="none", + num_slots=1, + ), + ) + + events = { + "reset_base": EventTermCfg( + func=dex_mdp.reset_root_state_uniform, + mode="reset", + params={ + "pose_range": {}, + "velocity_range": {}, + "asset_cfg": SceneEntityCfg("robot"), + }, + ), + "reset_robot_joints": EventTermCfg( + func=dex_mdp.reset_joints_by_offset, + mode="reset", + params={ + "position_range": (-0.03, 0.03), + "velocity_range": (0.0, 0.0), + "asset_cfg": SceneEntityCfg("robot", joint_names=(".*",)), + }, + ), + "reset_object_pose": EventTermCfg( + func=dex_mdp.reset_root_state_uniform, + mode="reset", + params={ + "asset_cfg": SceneEntityCfg("object"), + "pose_range": { + "x": (-0.006, 0.006), + "y": (-0.006, 0.006), + "z": (-0.005, 0.005), + "yaw": (-3.14, 3.14), + }, + "velocity_range": {}, + }, + ), + "randomize_object_mesh": EventTermCfg( + mode="reset", + func=dr.geom_dataid, + params={ + "mesh_ids": object_names_to_mesh_names(DEFAULT_DEX_OBJECTS), + "assignment_mode": "cycle", + "shared_random": True, + "asset_cfg": SceneEntityCfg("object", geom_names=("object_geom",)), + }, + ), + "dr_shared_contact_friction": EventTermCfg( + func=dex_mdp.randomize_shared_contact_friction, + mode="reset", + params={ + "friction_range": (0.6, 1.4), + "hand_cfg": SceneEntityCfg("robot", geom_names=(".*",)), + "object_cfg": SceneEntityCfg("object", geom_names=("object_geom",)), + "axes": (0,), + }, + ), + "dr_object_mass": EventTermCfg( + func=dex_mdp.randomize_body_mass, + mode="reset", + params={ + "mass_range": (0.7, 1.4), + "distribution": "uniform", + "operation": "scale", + "asset_cfg": SceneEntityCfg("object", body_names=("object",)), + }, + ), + "dr_robot_link_masses": EventTermCfg( + func=dex_mdp.randomize_body_mass, + mode="reset", + params={ + "mass_range": (0.8, 1.2), + "distribution": "uniform", + "operation": "scale", + "asset_cfg": SceneEntityCfg("robot", body_names=(".*",)), + }, + ), + } + + rewards = { + "rotate_finite_diff": RewardTermCfg( + func=dex_mdp.object_yaw_finite_diff_clipped, + weight=1.25, + params={ + "object_name": "object", + "clip_min": -0.25, + "clip_max": 0.25, + "history_steps": 4, + "negate_yaw_rate": True, + "drift_position_threshold": 0.02, + "drift_tilt_threshold": 0.35, + "drift_mode": "step", + "drift_inside_factor": 1.0, + "drift_outside_factor": 0.1, + }, + ), + "object_linvel_penalty": RewardTermCfg( + func=dex_mdp.object_linvel_l1, + weight=-0.3, + params={"object_name": "object"}, + ), + "pose_diff_penalty": RewardTermCfg( + func=dex_mdp.pose_diff_l2_from_reset, + weight=-0.1, + params={ + "asset_cfg": SceneEntityCfg("robot", joint_names=(".*",)), + "average_per_joint": False, + "joint_tolerance": 0.4, + }, + ), + "torque_penalty": RewardTermCfg( + func=dex_mdp.joint_torque_l2, + weight=-0.1, + params={"asset_cfg": SceneEntityCfg("robot", joint_names=(".*",))}, + ), + "work_penalty": RewardTermCfg( + func=dex_mdp.actuator_work_l2_penalty, + weight=-0.05, + params={"asset_cfg": SceneEntityCfg("robot", joint_names=(".*",))}, + ), + "object_fallen": RewardTermCfg( + func=dex_mdp.object_fallen, + weight=-10.0, + params={"object_name": "object", "minimum_height": 0.2}, + ), + } + + terminations = { + "time_out": TerminationTermCfg(func=dex_mdp.time_out, time_out=True), + "object_fell": TerminationTermCfg( + func=dex_mdp.root_height_below_minimum, + params={"minimum_height": 0.2, "asset_cfg": SceneEntityCfg("object")}, + ), + "object_too_fast": TerminationTermCfg( + func=dex_mdp.object_linear_speed_above, + params={"max_linear_speed": 1.0, "asset_cfg": SceneEntityCfg("object")}, + ), + "object_pose_deviation": TerminationTermCfg( + func=dex_mdp.object_pose_rp_position_deviation_from_reset, + params={ + "max_position_error": 0.08, + "max_tilt_error": 0.8, + "asset_cfg": SceneEntityCfg("object"), + }, + ), + "nan": TerminationTermCfg(func=dex_mdp.nan_detection), + } + + metrics = { + "reward_mean": MetricsTermCfg(func=dex_metrics.reward_mean), + "rotation_progress": MetricsTermCfg( + func=dex_metrics.object_rotation_progress, + params={ + "asset_cfg": SceneEntityCfg("object"), + "target_yaw_rate": 0.20, + "position_threshold": 0.02, + "tilt_threshold": 0.35, + }, + ), + "linear_speed": MetricsTermCfg( + func=dex_metrics.object_linear_speed, + params={"asset_cfg": SceneEntityCfg("object")}, + ), + "position_error": MetricsTermCfg( + func=dex_metrics.object_pose_rp_error_from_reset, + params={"component": "position", "asset_cfg": SceneEntityCfg("object")}, + ), + "tilt_error": MetricsTermCfg( + func=dex_metrics.object_pose_rp_error_from_reset, + params={"component": "tilt", "asset_cfg": SceneEntityCfg("object")}, + ), + "success": MetricsTermCfg( + func=dex_metrics.object_rotation_success, + params={ + "asset_cfg": SceneEntityCfg("object"), + "target_yaw_rate": 0.20, + "position_threshold": 0.02, + "tilt_threshold": 0.35, + }, + ), + } + + curriculum = { + "object_linvel_penalty_weight": CurriculumTermCfg( + func=dex_mdp.reward_weight_by_metric_progress, + params={ + "reward_name": "object_linvel_penalty", + "metric_name": "rotation_progress", + "progress_min": 0.05, + "progress_max": 0.25, + "weight_min": -0.03, + "weight_max": -0.3, + "ema_alpha": 0.08, + "weight_lerp": 0.15, + }, + ), + "pose_diff_penalty_weight": CurriculumTermCfg( + func=dex_mdp.reward_weight_by_metric_progress, + params={ + "reward_name": "pose_diff_penalty", + "metric_name": "rotation_progress", + "progress_min": 0.05, + "progress_max": 0.25, + "weight_min": -0.01, + "weight_max": -0.1, + "ema_alpha": 0.08, + "weight_lerp": 0.15, + }, + ), + "torque_penalty_weight": CurriculumTermCfg( + func=dex_mdp.reward_weight_by_metric_progress, + params={ + "reward_name": "torque_penalty", + "metric_name": "rotation_progress", + "progress_min": 0.05, + "progress_max": 0.25, + "weight_min": -0.1, + "weight_max": -1.0, + "ema_alpha": 0.08, + "weight_lerp": 0.15, + }, + ), + "work_penalty_weight": CurriculumTermCfg( + func=dex_mdp.reward_weight_by_metric_progress, + params={ + "reward_name": "work_penalty", + "metric_name": "rotation_progress", + "progress_min": 0.05, + "progress_max": 0.25, + "weight_min": -0.01, + "weight_max": -0.1, + "ema_alpha": 0.08, + "weight_lerp": 0.15, + }, + ), + } + + return ManagerBasedRlEnvCfg( + scene=SceneCfg( + terrain=TerrainEntityCfg(terrain_type="plane"), + entities={}, + sensors=sensors, + num_envs=1, + env_spacing=0.6, + ), + observations=observations, + actions=actions, + commands={}, + events=events, + rewards=rewards, + terminations=terminations, + metrics=metrics, + curriculum=curriculum, + viewer=ViewerConfig( + origin_type=ViewerConfig.OriginType.ASSET_BODY, + entity_name="robot", + body_name="palm", + distance=0.45, + elevation=-25, + azimuth=110, + ), + sim=SimulationCfg( + nconmax=55, + njmax=600, + contact_sensor_maxmatch=256, + mujoco=MujocoCfg( + timestep=0.005, + iterations=10, + ls_iterations=20, + impratio=10, + cone="elliptic", + ), + ), + decimation=10, + episode_length_s=20.0, + scale_rewards_by_dt=True, + ) + + +def apply_dex_manip_overrides( + cfg: ManagerBasedRlEnvCfg, + *, + objects: str | None, + envs_per_object: int | None, + assignment_mode: str | None = None, +) -> tuple[str, ...]: + object_names = parse_object_selection(objects) + mesh_names = object_names_to_mesh_names(object_names) + + event = cfg.events.get("randomize_object_mesh") + if event is None: + raise ValueError("Dex manip cfg is missing 'randomize_object_mesh' event.") + + event.params["mesh_ids"] = mesh_names + if assignment_mode is not None: + event.params["assignment_mode"] = assignment_mode + + if envs_per_object is not None: + if envs_per_object <= 0: + raise ValueError(f"envs_per_object must be > 0. Got {envs_per_object}.") + cfg.scene.num_envs = len(object_names) * envs_per_object + + return object_names + + +def dex_manip_env_cfg( + play: bool = False, + objects: str | None = None, + envs_per_object: int | None = 8, +) -> ManagerBasedRlEnvCfg: + cfg = make_dex_manip_base_env_cfg() + cfg.scene.entities = { + "robot": get_leap_left_custom_hand_cfg(), + "object": EntityCfg(spec_fn=get_multi_object_spec), + } + + selected_objects = apply_dex_manip_overrides( + cfg, + objects=objects, + envs_per_object=envs_per_object, + ) + selected_meshes = object_names_to_mesh_names(selected_objects) + for object_name, mesh_name in zip(selected_objects, selected_meshes, strict=True): + cfg.metrics[f"reward_{object_name.replace('-', '_')}"] = MetricsTermCfg( + func=dex_metrics.reward_for_mesh, + params={"mesh_name": mesh_name}, + ) + + if play: + cfg.episode_length_s = int(1e9) + cfg.observations["actor"].enable_corruption = False + cfg.curriculum = {} + + return cfg diff --git a/src/mjlab/tasks/dex_manip/mdp/__init__.py b/src/mjlab/tasks/dex_manip/mdp/__init__.py new file mode 100644 index 000000000..782677fcd --- /dev/null +++ b/src/mjlab/tasks/dex_manip/mdp/__init__.py @@ -0,0 +1,12 @@ +from mjlab.envs.mdp.curriculums import * # noqa: F401, F403 +from mjlab.envs.mdp.events import * # noqa: F401, F403 +from mjlab.envs.mdp.observations import * # noqa: F401, F403 +from mjlab.envs.mdp.rewards import * # noqa: F401, F403 +from mjlab.envs.mdp.terminations import * # noqa: F401, F403 + +from .actions import * # noqa: F401, F403 +from .curriculums import * # noqa: F401, F403 +from .events import * # noqa: F401, F403 +from .observations import * # noqa: F401, F403 +from .rewards import * # noqa: F401, F403 +from .terminations import * # noqa: F401, F403 diff --git a/src/mjlab/tasks/dex_manip/mdp/actions.py b/src/mjlab/tasks/dex_manip/mdp/actions.py new file mode 100644 index 000000000..e3ea263f9 --- /dev/null +++ b/src/mjlab/tasks/dex_manip/mdp/actions.py @@ -0,0 +1,108 @@ +from __future__ import annotations + +from dataclasses import dataclass +from typing import TYPE_CHECKING + +import torch + +from mjlab.actuator.actuator import TransmissionType +from mjlab.envs.mdp.actions.actions import BaseAction, BaseActionCfg + +if TYPE_CHECKING: + from mjlab.envs import ManagerBasedRlEnv + + +@dataclass(kw_only=True) +class JointPositionDeltaActionCfg(BaseActionCfg): + """Normalized delta joint-position control integrated on previous command.""" + + use_default_offset: bool = False + clip_to_joint_limits: bool = True + use_soft_joint_pos_limits: bool = True + delta_min: float = -0.03 + delta_max: float = 0.03 + interpolate_decimation: bool = False + + def __post_init__(self): + self.transmission_type = TransmissionType.JOINT + + def build(self, env: ManagerBasedRlEnv) -> JointPositionDeltaAction: + return JointPositionDeltaAction(self, env) + + +class JointPositionDeltaAction(BaseAction): + cfg: JointPositionDeltaActionCfg + + def __init__(self, cfg: JointPositionDeltaActionCfg, env: ManagerBasedRlEnv): + super().__init__(cfg=cfg, env=env) + if cfg.delta_max <= cfg.delta_min: + raise ValueError( + f"delta_max must be > delta_min, got ({cfg.delta_min}, {cfg.delta_max})" + ) + if cfg.scale != 1.0 or cfg.offset != 0.0: + raise ValueError( + "JointPositionDeltaAction expects raw normalized action in [-1, 1]. " + "Set scale=1.0 and offset=0.0." + ) + + self._target = torch.zeros_like(self._raw_actions) + self._prev_target = torch.zeros_like(self._raw_actions) + self._substep_counter = 0 + self._decimation = env.cfg.decimation + self._initialize_target(slice(None)) + + def _joint_limits( + self, env_ids: torch.Tensor | slice + ) -> tuple[torch.Tensor, torch.Tensor]: + if self.cfg.use_soft_joint_pos_limits: + limits = self._entity.data.soft_joint_pos_limits + else: + limits = self._entity.data.joint_pos_limits + lower = limits[env_ids][:, self._target_ids, 0] + upper = limits[env_ids][:, self._target_ids, 1] + return lower, upper + + def _apply_target_limits(self, env_ids: torch.Tensor | slice) -> None: + if not self.cfg.clip_to_joint_limits: + return + lower, upper = self._joint_limits(env_ids) + self._target[env_ids] = torch.clamp(self._target[env_ids], min=lower, max=upper) + + def _initialize_target(self, env_ids: torch.Tensor | slice) -> None: + if self.cfg.use_default_offset: + source = self._entity.data.default_joint_pos + else: + source = self._entity.data.joint_pos + self._target[env_ids] = source[env_ids][:, self._target_ids] + self._apply_target_limits(env_ids) + self._processed_actions[env_ids] = self._target[env_ids] + self._prev_target[env_ids] = self._target[env_ids] + + def process_actions(self, actions: torch.Tensor): + self._raw_actions[:] = actions + self._prev_target[:] = self._target + self._substep_counter = 0 + normalized = torch.clamp(self._raw_actions, min=-1.0, max=1.0) + delta = self.cfg.delta_min + 0.5 * (normalized + 1.0) * ( + self.cfg.delta_max - self.cfg.delta_min + ) + self._target = self._target + delta + self._apply_target_limits(slice(None)) + self._processed_actions = self._target + + def reset(self, env_ids: torch.Tensor | slice | None = None) -> None: + super().reset(env_ids) + if env_ids is None: + env_ids = slice(None) + self._initialize_target(env_ids) + + def apply_actions(self) -> None: + encoder_bias = self._entity.data.encoder_bias[:, self._target_ids] + if self.cfg.interpolate_decimation: + alpha = (self._substep_counter + 1) / self._decimation + interp = (1.0 - alpha) * self._prev_target + alpha * self._processed_actions + target = interp - encoder_bias + self._substep_counter += 1 + else: + target = self._processed_actions - encoder_bias + self._entity.set_joint_position_target(target, joint_ids=self._target_ids) diff --git a/src/mjlab/tasks/dex_manip/mdp/curriculums.py b/src/mjlab/tasks/dex_manip/mdp/curriculums.py new file mode 100644 index 000000000..2bd6b27f1 --- /dev/null +++ b/src/mjlab/tasks/dex_manip/mdp/curriculums.py @@ -0,0 +1,146 @@ +from __future__ import annotations + +from typing import TypedDict + +import torch + +from .numerics import finite_or_default, safe_mean_finite, sanitize_to_range + + +class RewardWeightStage(TypedDict): + step: int + weight: float + + +def reward_weight( + env, + env_ids: torch.Tensor, + reward_name: str, + weight_stages: list[RewardWeightStage], +) -> torch.Tensor: + del env_ids + reward_term_cfg = env.reward_manager.get_term_cfg(reward_name) + for stage in weight_stages: + if env.common_step_counter >= stage["step"]: + reward_term_cfg.weight = stage["weight"] + return torch.tensor([reward_term_cfg.weight], device=env.device) + + +class reward_weight_by_metric_progress: + def __init__(self, cfg, env): + del cfg, env + self._progress_ema: torch.Tensor | None = None + + def __call__( + self, + env, + env_ids: torch.Tensor | slice, + reward_name: str, + metric_name: str, + progress_min: float, + progress_max: float, + weight_min: float, + weight_max: float, + ema_alpha: float = 0.1, + weight_lerp: float = 0.2, + invert_metric: bool = False, + min_steps_per_episode: int = 8, + ) -> dict[str, torch.Tensor]: + if progress_max <= progress_min: + raise ValueError( + f"progress_max ({progress_max}) must be > progress_min ({progress_min})." + ) + + ema_alpha = float(torch.clamp(torch.tensor(ema_alpha), min=0.0, max=1.0).item()) + weight_lerp = float( + torch.clamp(torch.tensor(weight_lerp), min=0.0, max=1.0).item() + ) + + reward_term_cfg = env.reward_manager.get_term_cfg(reward_name) + + if metric_name not in env.metrics_manager._episode_sums: + current_weight = finite_or_default(float(reward_term_cfg.weight), weight_min) + reward_term_cfg.weight = current_weight + return { + "weight": torch.tensor(current_weight, device=env.device), + "progress_ema": torch.tensor(0.0, device=env.device), + } + + episode_sums = env.metrics_manager._episode_sums[metric_name][env_ids] + step_counts = env.metrics_manager._step_count[env_ids].float() + valid_mask = step_counts >= float(min_steps_per_episode) + if not torch.any(valid_mask): + current_weight = finite_or_default(float(reward_term_cfg.weight), weight_min) + reward_term_cfg.weight = current_weight + return { + "weight": torch.tensor(current_weight, device=env.device), + "progress_ema": ( + self._progress_ema + if self._progress_ema is not None + else torch.tensor(0.0, device=env.device) + ), + } + + metric_samples = episode_sums[valid_mask] / torch.clamp( + step_counts[valid_mask], min=1.0 + ) + metric_avg = safe_mean_finite(metric_samples) + if metric_avg is None: + current_weight = finite_or_default(float(reward_term_cfg.weight), weight_min) + reward_term_cfg.weight = current_weight + return { + "weight": torch.tensor(current_weight, device=env.device), + "progress_ema": ( + self._progress_ema + if self._progress_ema is not None + else torch.tensor(0.0, device=env.device) + ), + } + + progress_raw = sanitize_to_range( + (metric_avg - progress_min) / (progress_max - progress_min), + 0.0, + 1.0, + nan_default=0.0, + ) + if invert_metric: + progress_raw = 1.0 - progress_raw + + if self._progress_ema is None or not torch.isfinite(self._progress_ema).item(): + self._progress_ema = progress_raw.detach() + else: + self._progress_ema = ( + (1.0 - ema_alpha) * self._progress_ema + ema_alpha * progress_raw.detach() + ) + self._progress_ema = sanitize_to_range( + self._progress_ema, + 0.0, + 1.0, + nan_default=0.0, + ) + + target_weight = sanitize_to_range( + weight_min + self._progress_ema * (weight_max - weight_min), + weight_min, + weight_max, + nan_default=weight_min, + ) + current_weight = torch.tensor( + finite_or_default(float(reward_term_cfg.weight), float(target_weight.item())), + device=env.device, + ) + new_weight = sanitize_to_range( + (1.0 - weight_lerp) * current_weight + weight_lerp * target_weight, + weight_min, + weight_max, + nan_default=float(target_weight.item()), + ) + reward_term_cfg.weight = float(new_weight.item()) + + return { + "metric": metric_avg.detach(), + "progress_raw": progress_raw.detach(), + "progress_ema": self._progress_ema.detach(), + "target_weight": target_weight.detach(), + "weight": new_weight.detach(), + } diff --git a/src/mjlab/tasks/dex_manip/mdp/events.py b/src/mjlab/tasks/dex_manip/mdp/events.py new file mode 100644 index 000000000..caf0912b3 --- /dev/null +++ b/src/mjlab/tasks/dex_manip/mdp/events.py @@ -0,0 +1,156 @@ +from __future__ import annotations + +from typing import TYPE_CHECKING + +import torch + +from mjlab.entity import Entity +from mjlab.managers.event_manager import requires_model_fields +from mjlab.managers.scene_entity_config import SceneEntityCfg +from mjlab.utils.lab_api.math import sample_gaussian, sample_log_uniform, sample_uniform + +if TYPE_CHECKING: + from mjlab.envs import ManagerBasedRlEnv + + +_DEFAULT_OBJECT_CFG = SceneEntityCfg("object", geom_names=("object_geom",)) +_DEFAULT_HAND_GEOM_CFG = SceneEntityCfg("robot", geom_names=(".*",)) + + +def _resolve_env_ids( + env: ManagerBasedRlEnv, env_ids: torch.Tensor | slice | None +) -> torch.Tensor: + if env_ids is None: + return torch.arange(env.num_envs, device=env.device, dtype=torch.int) + if isinstance(env_ids, slice): + start, stop, step = env_ids.indices(env.num_envs) + return torch.arange(start, stop, step, device=env.device, dtype=torch.int) + return env_ids.to(env.device, dtype=torch.int) + + +def _resolve_geom_ids(env: ManagerBasedRlEnv, asset_cfg: SceneEntityCfg) -> torch.Tensor: + if asset_cfg.geom_ids is None: + raise ValueError("asset_cfg.geom_ids must be resolved.") + if isinstance(asset_cfg.geom_ids, list): + return torch.tensor(asset_cfg.geom_ids, device=env.device, dtype=torch.int) + if isinstance(asset_cfg.geom_ids, torch.Tensor): + return asset_cfg.geom_ids.to(env.device, dtype=torch.int) + if isinstance(asset_cfg.geom_ids, slice): + entity: Entity = env.scene[asset_cfg.name] + return entity.indexing.geom_ids[asset_cfg.geom_ids] + return torch.tensor([asset_cfg.geom_ids], device=env.device, dtype=torch.int) + + +def _resolve_body_ids(env: ManagerBasedRlEnv, asset_cfg: SceneEntityCfg) -> torch.Tensor: + if asset_cfg.body_ids is None: + raise ValueError("asset_cfg.body_ids must be resolved.") + if isinstance(asset_cfg.body_ids, list): + return torch.tensor(asset_cfg.body_ids, device=env.device, dtype=torch.int) + if isinstance(asset_cfg.body_ids, torch.Tensor): + return asset_cfg.body_ids.to(env.device, dtype=torch.int) + if isinstance(asset_cfg.body_ids, slice): + entity: Entity = env.scene[asset_cfg.name] + return entity.indexing.body_ids[asset_cfg.body_ids] + return torch.tensor([asset_cfg.body_ids], device=env.device, dtype=torch.int) + + +def _sample_distribution( + distribution: str, + lower: float, + upper: float, + shape: tuple[int, ...], + device: str, +) -> torch.Tensor: + low = torch.tensor(lower, device=device, dtype=torch.float32) + high = torch.tensor(upper, device=device, dtype=torch.float32) + if distribution == "uniform": + return sample_uniform(low, high, shape, device=device) + if distribution == "log_uniform": + return sample_log_uniform(low, high, shape, device=device) + if distribution == "gaussian": + return sample_gaussian(low, high, shape, device=device) + raise ValueError(f"Unsupported distribution: {distribution}") + + +@requires_model_fields("geom_friction") +def randomize_shared_contact_friction( + env: ManagerBasedRlEnv, + env_ids: torch.Tensor | slice | None, + friction_range: tuple[float, float], + hand_cfg: SceneEntityCfg = _DEFAULT_HAND_GEOM_CFG, + object_cfg: SceneEntityCfg = _DEFAULT_OBJECT_CFG, + axes: tuple[int, ...] = (0,), +) -> None: + env_ids = _resolve_env_ids(env, env_ids) + if len(env_ids) == 0: + return + + hand_geom_ids = _resolve_geom_ids(env, hand_cfg) + object_geom_ids = _resolve_geom_ids(env, object_cfg) + if len(hand_geom_ids) == 0 or len(object_geom_ids) == 0: + return + + friction = sample_uniform( + friction_range[0], + friction_range[1], + (len(env_ids), 1), + device=env.device, + ) + + for axis in axes: + env.sim.model.geom_friction[env_ids[:, None], hand_geom_ids[None, :], axis] = friction + env.sim.model.geom_friction[env_ids[:, None], object_geom_ids[None, :], axis] = friction + + +@requires_model_fields("body_mass") +def randomize_body_mass( + env: ManagerBasedRlEnv, + env_ids: torch.Tensor | slice | None, + mass_range: tuple[float, float], + asset_cfg: SceneEntityCfg, + distribution: str = "uniform", + operation: str = "scale", + min_mass: float = 1e-5, +) -> None: + env_ids = _resolve_env_ids(env, env_ids) + if len(env_ids) == 0: + return + body_ids = _resolve_body_ids(env, asset_cfg) + if len(body_ids) == 0: + return + + current = env.sim.model.body_mass[env_ids[:, None], body_ids[None, :]] + if operation == "scale": + base = env.sim.get_default_field("body_mass")[body_ids].unsqueeze(0).expand_as(current) + scale = _sample_distribution( + distribution=distribution, + lower=mass_range[0], + upper=mass_range[1], + shape=current.shape, + device=env.device, + ) + out = base * scale + elif operation == "abs": + out = _sample_distribution( + distribution=distribution, + lower=mass_range[0], + upper=mass_range[1], + shape=current.shape, + device=env.device, + ) + elif operation == "add": + base = env.sim.get_default_field("body_mass")[body_ids].unsqueeze(0).expand_as(current) + delta = _sample_distribution( + distribution=distribution, + lower=mass_range[0], + upper=mass_range[1], + shape=current.shape, + device=env.device, + ) + out = base + delta + else: + raise ValueError(f"Unsupported operation '{operation}'.") + + env.sim.model.body_mass[env_ids[:, None], body_ids[None, :]] = torch.clamp( + out, min=min_mass + ) diff --git a/src/mjlab/tasks/dex_manip/mdp/numerics.py b/src/mjlab/tasks/dex_manip/mdp/numerics.py new file mode 100644 index 000000000..3582fd92b --- /dev/null +++ b/src/mjlab/tasks/dex_manip/mdp/numerics.py @@ -0,0 +1,28 @@ +from __future__ import annotations + +import math + +import torch + + +def sanitize_to_range( + value: torch.Tensor | float, + lower: float, + upper: float, + *, + nan_default: float, +) -> torch.Tensor: + tensor = value if isinstance(value, torch.Tensor) else torch.tensor(float(value)) + tensor = torch.nan_to_num(tensor, nan=nan_default, posinf=upper, neginf=lower) + return torch.clamp(tensor, min=lower, max=upper) + + +def finite_or_default(value: float, default: float) -> float: + return value if math.isfinite(value) else default + + +def safe_mean_finite(values: torch.Tensor) -> torch.Tensor | None: + finite = values[torch.isfinite(values)] + if finite.numel() == 0: + return None + return finite.mean() diff --git a/src/mjlab/tasks/dex_manip/mdp/observations.py b/src/mjlab/tasks/dex_manip/mdp/observations.py new file mode 100644 index 000000000..fd70657f2 --- /dev/null +++ b/src/mjlab/tasks/dex_manip/mdp/observations.py @@ -0,0 +1,236 @@ +from __future__ import annotations + +from typing import TYPE_CHECKING + +import torch + +from mjlab.entity import Entity +from mjlab.managers.scene_entity_config import SceneEntityCfg +from mjlab.utils.lab_api.math import quat_apply, quat_inv, quat_mul + +if TYPE_CHECKING: + from mjlab.envs import ManagerBasedRlEnv + + +_DEFAULT_HAND_CFG = SceneEntityCfg("robot", body_names=("palm",)) +_DEFAULT_PALM_CENTER_GEOM_EXPR = "palm_collision_.*" +_DEFAULT_JOINT_ASSET_CFG = SceneEntityCfg("robot", joint_names=(".*",)) + + +def _joint_position_command( + env: ManagerBasedRlEnv, + asset_cfg: SceneEntityCfg, + action_name: str = "joint_pos", +) -> torch.Tensor: + asset: Entity = env.scene[asset_cfg.name] + joint_ids = asset_cfg.joint_ids + term = env.action_manager.get_term(action_name) + + target_ids = getattr(term, "target_ids", None) + command_value = getattr(term, "_target", None) + if command_value is None: + command_value = getattr(term, "_processed_actions", None) + if target_ids is None or command_value is None: + return asset.data.joint_pos_target[:, joint_ids] + + target_ids = target_ids.to(device=env.device, dtype=torch.long) + commanded_full = asset.data.joint_pos.clone() + commanded_full[:, target_ids] = command_value + return commanded_full[:, joint_ids] + + +def joint_pos_commanded( + env: ManagerBasedRlEnv, + action_name: str = "joint_pos", + asset_cfg: SceneEntityCfg = _DEFAULT_JOINT_ASSET_CFG, +) -> torch.Tensor: + return _joint_position_command(env=env, asset_cfg=asset_cfg, action_name=action_name) + + +def joint_pos_command_error( + env: ManagerBasedRlEnv, + action_name: str = "joint_pos", + biased: bool = True, + asset_cfg: SceneEntityCfg = _DEFAULT_JOINT_ASSET_CFG, +) -> torch.Tensor: + asset: Entity = env.scene[asset_cfg.name] + joint_ids = asset_cfg.joint_ids + commanded = _joint_position_command(env=env, asset_cfg=asset_cfg, action_name=action_name) + measured = asset.data.joint_pos_biased if biased else asset.data.joint_pos + return commanded - measured[:, joint_ids] + + +def palm_center_pose_w( + env: ManagerBasedRlEnv, + hand_cfg: SceneEntityCfg = _DEFAULT_HAND_CFG, + palm_center_geom_expr: str = _DEFAULT_PALM_CENTER_GEOM_EXPR, +) -> tuple[torch.Tensor, torch.Tensor]: + hand: Entity = env.scene[hand_cfg.name] + palm_pos_w = hand.data.body_link_pos_w[:, hand_cfg.body_ids].squeeze(1) + palm_quat_w = hand.data.body_link_quat_w[:, hand_cfg.body_ids].squeeze(1) + + cache_key = f"_dex_manip_palm_center_geom_ids::{hand_cfg.name}::{palm_center_geom_expr}" + palm_geom_ids = getattr(env, cache_key, None) + if palm_geom_ids is None: + palm_geom_ids, _ = hand.find_geoms(palm_center_geom_expr, preserve_order=True) + palm_geom_ids = torch.tensor(palm_geom_ids, dtype=torch.long, device=env.device) + setattr(env, cache_key, palm_geom_ids) + + if palm_geom_ids.numel() == 0: + return palm_pos_w, palm_quat_w + + palm_center_w = hand.data.geom_pos_w[:, palm_geom_ids].mean(dim=1) + return palm_center_w, palm_quat_w + + +def object_pose_in_palm_frame( + env: ManagerBasedRlEnv, + object_name: str, + hand_cfg: SceneEntityCfg = _DEFAULT_HAND_CFG, + palm_center_geom_expr: str = _DEFAULT_PALM_CENTER_GEOM_EXPR, +) -> torch.Tensor: + obj: Entity = env.scene[object_name] + palm_center_w, palm_quat_w = palm_center_pose_w( + env=env, + hand_cfg=hand_cfg, + palm_center_geom_expr=palm_center_geom_expr, + ) + q_inv = quat_inv(palm_quat_w) + obj_pos_w = obj.data.root_link_pos_w + obj_quat_w = obj.data.root_link_quat_w + pos_palm = quat_apply(q_inv, obj_pos_w - palm_center_w) + quat_palm = quat_mul(q_inv, obj_quat_w) + return torch.cat([pos_palm, quat_palm], dim=-1) + + +def object_lin_vel_in_palm_frame( + env: ManagerBasedRlEnv, + object_name: str, + hand_cfg: SceneEntityCfg = _DEFAULT_HAND_CFG, + palm_center_geom_expr: str = _DEFAULT_PALM_CENTER_GEOM_EXPR, +) -> torch.Tensor: + hand: Entity = env.scene[hand_cfg.name] + obj: Entity = env.scene[object_name] + palm_pos_w = hand.data.body_link_pos_w[:, hand_cfg.body_ids].squeeze(1) + palm_quat_w = hand.data.body_link_quat_w[:, hand_cfg.body_ids].squeeze(1) + palm_vel_w = hand.data.body_link_vel_w[:, hand_cfg.body_ids].squeeze(1) + palm_lin_vel_w = palm_vel_w[:, :3] + palm_ang_vel_w = palm_vel_w[:, 3:] + + palm_center_w, _ = palm_center_pose_w( + env=env, + hand_cfg=hand_cfg, + palm_center_geom_expr=palm_center_geom_expr, + ) + center_offset_w = palm_center_w - palm_pos_w + palm_center_lin_vel_w = palm_lin_vel_w + torch.cross( + palm_ang_vel_w, center_offset_w, dim=-1 + ) + + obj_lin_vel_w = obj.data.root_link_lin_vel_w + rel_lin_vel_w = obj_lin_vel_w - palm_center_lin_vel_w + return quat_apply(quat_inv(palm_quat_w), rel_lin_vel_w) + + +def object_ang_vel_in_palm_frame( + env: ManagerBasedRlEnv, + object_name: str, + hand_cfg: SceneEntityCfg = _DEFAULT_HAND_CFG, + palm_center_geom_expr: str = _DEFAULT_PALM_CENTER_GEOM_EXPR, +) -> torch.Tensor: + del palm_center_geom_expr + hand: Entity = env.scene[hand_cfg.name] + obj: Entity = env.scene[object_name] + palm_quat_w = hand.data.body_link_quat_w[:, hand_cfg.body_ids].squeeze(1) + palm_ang_vel_w = hand.data.body_link_vel_w[:, hand_cfg.body_ids].squeeze(1)[:, 3:] + obj_ang_vel_w = obj.data.root_link_ang_vel_w + rel_ang_vel_w = obj_ang_vel_w - palm_ang_vel_w + return quat_apply(quat_inv(palm_quat_w), rel_ang_vel_w) + + +def object_size( + env: ManagerBasedRlEnv, + object_name: str, + geom_name: str = "object_geom", +) -> torch.Tensor: + obj: Entity = env.scene[object_name] + cache_key = f"_dex_manip_size_geom_id::{object_name}::{geom_name}" + geom_world_id = getattr(env, cache_key, None) + if geom_world_id is None: + geom_local_ids, _ = obj.find_geoms(geom_name, preserve_order=True) + if len(geom_local_ids) != 1: + raise ValueError( + f"Expected exactly one object geom matching '{geom_name}', got {len(geom_local_ids)}." + ) + geom_world_id = int(obj.indexing.geom_ids[geom_local_ids[0]].item()) + setattr(env, cache_key, geom_world_id) + + size = env.sim.model.geom_size[:, geom_world_id, 0] + if torch.any(size <= 0.0): + rbound = env.sim.model.geom_rbound[:, geom_world_id] + fallback_size = rbound / (3.0**0.5) + size = torch.where(size > 0.0, size, fallback_size) + return size.unsqueeze(-1) + + +def object_mass( + env: ManagerBasedRlEnv, + object_name: str, + body_name: str = "object", +) -> torch.Tensor: + obj: Entity = env.scene[object_name] + cache_key = f"_dex_manip_mass_body_id::{object_name}::{body_name}" + body_world_id = getattr(env, cache_key, None) + if body_world_id is None: + body_local_ids, _ = obj.find_bodies(body_name, preserve_order=True) + if len(body_local_ids) != 1: + raise ValueError( + f"Expected exactly one object body matching '{body_name}', got {len(body_local_ids)}." + ) + body_world_id = int(obj.indexing.body_ids[body_local_ids[0]].item()) + setattr(env, cache_key, body_world_id) + + mass = env.sim.model.body_mass[:, body_world_id] + return mass.unsqueeze(-1) + + +def object_com_offset_b( + env: ManagerBasedRlEnv, + object_name: str, + body_name: str = "object", +) -> torch.Tensor: + obj: Entity = env.scene[object_name] + cache_key = f"_dex_manip_com_body_id::{object_name}::{body_name}" + body_world_id = getattr(env, cache_key, None) + if body_world_id is None: + body_local_ids, _ = obj.find_bodies(body_name, preserve_order=True) + if len(body_local_ids) != 1: + raise ValueError( + f"Expected exactly one object body matching '{body_name}', got {len(body_local_ids)}." + ) + body_world_id = int(obj.indexing.body_ids[body_local_ids[0]].item()) + setattr(env, cache_key, body_world_id) + + return env.sim.model.body_ipos[:, body_world_id, :] + + +def object_friction_coeff( + env: ManagerBasedRlEnv, + object_name: str, + geom_name: str = "object_geom", + axis: int = 0, +) -> torch.Tensor: + obj: Entity = env.scene[object_name] + cache_key = f"_dex_manip_friction_geom_id::{object_name}::{geom_name}" + geom_world_id = getattr(env, cache_key, None) + if geom_world_id is None: + geom_local_ids, _ = obj.find_geoms(geom_name, preserve_order=True) + if len(geom_local_ids) != 1: + raise ValueError( + f"Expected exactly one object geom matching '{geom_name}', got {len(geom_local_ids)}." + ) + geom_world_id = int(obj.indexing.geom_ids[geom_local_ids[0]].item()) + setattr(env, cache_key, geom_world_id) + + friction = env.sim.model.geom_friction[:, geom_world_id, axis] + return friction.unsqueeze(-1) diff --git a/src/mjlab/tasks/dex_manip/mdp/rewards.py b/src/mjlab/tasks/dex_manip/mdp/rewards.py new file mode 100644 index 000000000..97e6612ff --- /dev/null +++ b/src/mjlab/tasks/dex_manip/mdp/rewards.py @@ -0,0 +1,243 @@ +from __future__ import annotations + +from typing import TYPE_CHECKING + +import torch + +from mjlab.entity import Entity +from mjlab.managers.reward_manager import RewardTermCfg +from mjlab.managers.scene_entity_config import SceneEntityCfg +from mjlab.utils.lab_api.math import euler_xyz_from_quat, wrap_to_pi + +if TYPE_CHECKING: + from mjlab.envs import ManagerBasedRlEnv + + +_DEFAULT_ROBOT_JOINT_CFG = SceneEntityCfg("robot", joint_names=(".*",)) + + +def actuator_work_l2_penalty( + env: ManagerBasedRlEnv, + asset_cfg: SceneEntityCfg = _DEFAULT_ROBOT_JOINT_CFG, + use_joint_acc: bool = False, + max_abs_power: float | None = None, + max_abs_torque: float | None = None, + normalize: bool = False, +) -> torch.Tensor: + asset: Entity = env.scene[asset_cfg.name] + tau = asset.data.actuator_force[:, asset_cfg.joint_ids] + if max_abs_torque is not None: + tau = torch.clamp(tau, min=-max_abs_torque, max=max_abs_torque) + motion_term = ( + asset.data.joint_acc[:, asset_cfg.joint_ids] + if use_joint_acc + else asset.data.joint_vel[:, asset_cfg.joint_ids] + ) + mech_power = torch.sum(tau * motion_term, dim=-1) + if max_abs_power is not None: + mech_power = torch.clamp(mech_power, min=-max_abs_power, max=max_abs_power) + work = torch.square(mech_power) + if normalize and max_abs_power is not None and max_abs_power > 0.0: + work = work / (max_abs_power**2) + return work + + +class object_yaw_finite_diff_clipped: + def __init__(self, cfg: RewardTermCfg, env: ManagerBasedRlEnv): + self.object_name = cfg.params["object_name"] + self.history_steps = int(cfg.params.get("history_steps", 1)) + if self.history_steps < 1: + raise ValueError(f"history_steps must be >= 1, got {self.history_steps}") + self.prev_yaw = torch.zeros(env.num_envs, device=env.device) + self._env_ids = torch.arange(env.num_envs, device=env.device, dtype=torch.long) + self._delta_hist = torch.zeros((env.num_envs, self.history_steps), device=env.device) + self._hist_ptr = torch.zeros(env.num_envs, device=env.device, dtype=torch.long) + self._hist_count = torch.zeros(env.num_envs, device=env.device, dtype=torch.long) + self._init_pos_w = torch.zeros((env.num_envs, 3), device=env.device) + self._init_roll = torch.zeros(env.num_envs, device=env.device) + self._init_pitch = torch.zeros(env.num_envs, device=env.device) + self._has_init = torch.zeros(env.num_envs, device=env.device, dtype=torch.bool) + self._env = env + self.reset(None) + + def _current_yaw(self) -> torch.Tensor: + obj: Entity = self._env.scene[self.object_name] + _, _, yaw = euler_xyz_from_quat(obj.data.root_link_quat_w) + return yaw + + def reset(self, env_ids: torch.Tensor | slice | None = None): + if env_ids is None: + env_ids = slice(None) + obj: Entity = self._env.scene[self.object_name] + yaw = self._current_yaw() + self.prev_yaw[env_ids] = yaw[env_ids] + self._delta_hist[env_ids] = 0.0 + self._hist_ptr[env_ids] = 0 + self._hist_count[env_ids] = 0 + roll, pitch, _ = euler_xyz_from_quat(obj.data.root_link_quat_w) + self._init_pos_w[env_ids] = obj.data.root_link_pos_w[env_ids] + self._init_roll[env_ids] = roll[env_ids] + self._init_pitch[env_ids] = pitch[env_ids] + self._has_init[env_ids] = True + + def __call__( + self, + env: ManagerBasedRlEnv, + object_name: str, + clip_min: float = -0.25, + clip_max: float = 0.25, + history_steps: int | None = None, + negate_yaw_rate: bool = True, + drift_position_threshold: float = 0.02, + drift_tilt_threshold: float = 0.35, + drift_inside_factor: float = 1.0, + drift_outside_factor: float = 0.1, + drift_mode: str = "step", + drift_decay_k_pos: float = 30.0, + drift_decay_k_tilt: float = 3.0, + drift_min_factor: float = 0.1, + ) -> torch.Tensor: + del object_name + if history_steps is not None and history_steps != self.history_steps: + raise ValueError( + f"history_steps={history_steps} does not match initialized value {self.history_steps}." + ) + + yaw = self._current_yaw() + reset_mask = env.episode_length_buf <= 1 + if torch.any(reset_mask): + self.reset(reset_mask.nonzero(as_tuple=False).squeeze(-1)) + + delta_yaw = wrap_to_pi(yaw - self.prev_yaw) + self.prev_yaw[:] = yaw + + hist_ptr = self._hist_ptr + self._delta_hist[self._env_ids, hist_ptr] = delta_yaw + self._hist_ptr = (hist_ptr + 1) % self.history_steps + self._hist_count = torch.clamp(self._hist_count + 1, max=self.history_steps) + + hist_len = torch.clamp(self._hist_count, min=1).to(delta_yaw.dtype) + avg_delta_yaw = self._delta_hist.sum(dim=-1) / hist_len + + yaw_rate = avg_delta_yaw / max(env.step_dt, 1e-6) + if negate_yaw_rate: + yaw_rate = -yaw_rate + yaw_reward = torch.clamp(yaw_rate, min=clip_min, max=clip_max) + + obj: Entity = self._env.scene[self.object_name] + roll, pitch, _ = euler_xyz_from_quat(obj.data.root_link_quat_w) + pos_error = torch.linalg.vector_norm(obj.data.root_link_pos_w - self._init_pos_w, dim=-1) + roll_error = wrap_to_pi(roll - self._init_roll).abs() + pitch_error = wrap_to_pi(pitch - self._init_pitch).abs() + tilt_error = torch.linalg.vector_norm( + torch.stack([roll_error, pitch_error], dim=-1), dim=-1 + ) + + if drift_mode == "step": + in_bounds = (pos_error <= drift_position_threshold) & ( + tilt_error <= drift_tilt_threshold + ) + drift_factor = torch.where( + in_bounds, + torch.full_like(yaw_reward, drift_inside_factor), + torch.full_like(yaw_reward, drift_outside_factor), + ) + elif drift_mode == "exp": + pos_excess = torch.clamp(pos_error - drift_position_threshold, min=0.0) + tilt_excess = torch.clamp(tilt_error - drift_tilt_threshold, min=0.0) + drift_factor = torch.exp( + -drift_decay_k_pos * pos_excess - drift_decay_k_tilt * tilt_excess + ) + drift_factor = torch.clamp(drift_factor, min=drift_min_factor, max=1.0) + else: + raise ValueError( + f"Unknown drift_mode '{drift_mode}'. Expected one of: 'step', 'exp'." + ) + + drift_factor = torch.where(self._has_init, drift_factor, torch.ones_like(drift_factor)) + return yaw_reward * drift_factor + + +def object_linvel_l1( + env: ManagerBasedRlEnv, + object_name: str, + clip_max: float | None = None, + normalize: bool = False, +) -> torch.Tensor: + obj: Entity = env.scene[object_name] + value = torch.norm(obj.data.root_link_lin_vel_w, p=1, dim=-1) + if clip_max is not None: + value = torch.clamp(value, max=clip_max) + if normalize and clip_max is not None and clip_max > 0.0: + value = value / clip_max + return value + + +def object_fallen( + env: ManagerBasedRlEnv, + object_name: str, + minimum_height: float, +) -> torch.Tensor: + obj: Entity = env.scene[object_name] + return (obj.data.root_link_pos_w[:, 2] < minimum_height).float() + + +def joint_torque_l2( + env: ManagerBasedRlEnv, + asset_cfg: SceneEntityCfg = _DEFAULT_ROBOT_JOINT_CFG, + max_abs_torque: float | None = None, + normalize: bool = False, +) -> torch.Tensor: + asset: Entity = env.scene[asset_cfg.name] + tau = asset.data.actuator_force[:, asset_cfg.joint_ids] + if max_abs_torque is not None: + tau = torch.clamp(tau, min=-max_abs_torque, max=max_abs_torque) + value = torch.sum(torch.square(tau), dim=-1) + if normalize: + if max_abs_torque is not None and max_abs_torque > 0.0: + denom = tau.shape[1] * (max_abs_torque**2) + else: + denom = max(tau.shape[1], 1) + value = value / denom + return value + + +class pose_diff_l2_from_reset: + def __init__(self, cfg: RewardTermCfg, env: ManagerBasedRlEnv): + self.asset_cfg: SceneEntityCfg = cfg.params["asset_cfg"] + self.asset: Entity = env.scene[self.asset_cfg.name] + num_joints = self.asset.data.joint_pos[:, self.asset_cfg.joint_ids].shape[1] + self.init_joint_pos = torch.zeros( + (env.num_envs, num_joints), + device=env.device, + dtype=self.asset.data.joint_pos.dtype, + ) + self.reset(None) + + def reset(self, env_ids: torch.Tensor | slice | None = None): + if env_ids is None: + env_ids = slice(None) + self.init_joint_pos[env_ids] = self.asset.data.joint_pos[env_ids][ + :, self.asset_cfg.joint_ids + ] + + def __call__( + self, + env: ManagerBasedRlEnv, + asset_cfg: SceneEntityCfg, + average_per_joint: bool = True, + joint_tolerance: float = 0.0, + clip_max: float | None = None, + normalize: bool = False, + ) -> torch.Tensor: + del env, asset_cfg, normalize + q = self.asset.data.joint_pos[:, self.asset_cfg.joint_ids] + joint_diff = torch.abs(q - self.init_joint_pos) + if joint_tolerance > 0.0: + joint_diff = torch.clamp(joint_diff - joint_tolerance, min=0.0) + value = torch.sum(torch.square(joint_diff), dim=-1) + if average_per_joint: + value = value / max(q.shape[1], 1) + if clip_max is not None: + value = torch.clamp(value, max=clip_max) + return value diff --git a/src/mjlab/tasks/dex_manip/mdp/terminations.py b/src/mjlab/tasks/dex_manip/mdp/terminations.py new file mode 100644 index 000000000..baf7f2467 --- /dev/null +++ b/src/mjlab/tasks/dex_manip/mdp/terminations.py @@ -0,0 +1,65 @@ +from __future__ import annotations + +from typing import TYPE_CHECKING + +import torch + +from mjlab.managers.scene_entity_config import SceneEntityCfg +from mjlab.utils.lab_api.math import euler_xyz_from_quat, wrap_to_pi + +if TYPE_CHECKING: + from mjlab.entity import Entity + from mjlab.envs import ManagerBasedRlEnv + + +_DEFAULT_OBJECT_CFG = SceneEntityCfg("object") + + +def object_linear_speed_above( + env: ManagerBasedRlEnv, + max_linear_speed: float, + asset_cfg: SceneEntityCfg = _DEFAULT_OBJECT_CFG, +) -> torch.Tensor: + asset: Entity = env.scene[asset_cfg.name] + speed = torch.linalg.vector_norm(asset.data.root_link_lin_vel_w, dim=-1) + return speed > max_linear_speed + + +class object_pose_rp_position_deviation_from_reset: + def __init__(self, cfg, env: ManagerBasedRlEnv): + self._asset_cfg: SceneEntityCfg = cfg.params.get("asset_cfg", _DEFAULT_OBJECT_CFG) + self._asset: Entity = env.scene[self._asset_cfg.name] + self._init_pos_w = torch.zeros((env.num_envs, 3), device=env.device) + self._init_roll = torch.zeros(env.num_envs, device=env.device) + self._init_pitch = torch.zeros(env.num_envs, device=env.device) + self._has_init = torch.zeros(env.num_envs, device=env.device, dtype=torch.bool) + + def reset(self, env_ids: torch.Tensor | slice | None = None): + if env_ids is None: + env_ids = slice(None) + pos_w = self._asset.data.root_link_pos_w + roll, pitch, _ = euler_xyz_from_quat(self._asset.data.root_link_quat_w) + self._init_pos_w[env_ids] = pos_w[env_ids] + self._init_roll[env_ids] = roll[env_ids] + self._init_pitch[env_ids] = pitch[env_ids] + self._has_init[env_ids] = True + + def __call__( + self, + env: ManagerBasedRlEnv, + max_position_error: float, + max_tilt_error: float, + asset_cfg: SceneEntityCfg = _DEFAULT_OBJECT_CFG, + ) -> torch.Tensor: + del env, asset_cfg + pos_w = self._asset.data.root_link_pos_w + roll, pitch, _ = euler_xyz_from_quat(self._asset.data.root_link_quat_w) + pos_error = torch.linalg.vector_norm(pos_w - self._init_pos_w, dim=-1) + roll_error = wrap_to_pi(roll - self._init_roll).abs() + pitch_error = wrap_to_pi(pitch - self._init_pitch).abs() + tilt_error = torch.linalg.vector_norm( + torch.stack([roll_error, pitch_error], dim=-1), dim=-1 + ) + return self._has_init & ( + (pos_error > max_position_error) | (tilt_error > max_tilt_error) + ) diff --git a/src/mjlab/tasks/dex_manip/metrics.py b/src/mjlab/tasks/dex_manip/metrics.py new file mode 100644 index 000000000..c9e244389 --- /dev/null +++ b/src/mjlab/tasks/dex_manip/metrics.py @@ -0,0 +1,238 @@ +from __future__ import annotations + +from typing import TYPE_CHECKING, Literal, cast + +import torch + +from mjlab.managers.scene_entity_config import SceneEntityCfg +from mjlab.utils.lab_api.math import euler_xyz_from_quat, wrap_to_pi + +from .mdp.numerics import sanitize_to_range + +if TYPE_CHECKING: + from mjlab.entity import Entity + from mjlab.envs import ManagerBasedRlEnv + + +_DEFAULT_OBJECT_CFG = SceneEntityCfg("object", geom_names=("object_geom",)) +_DEFAULT_OBJECT_BODY_CFG = SceneEntityCfg("object") + + +def _resolve_object_geom_id( + env: ManagerBasedRlEnv, + object_cfg: SceneEntityCfg, +) -> int: + cache_key = f"_dex_manip_object_geom_id::{object_cfg.name}::{object_cfg.geom_names}" + cached = getattr(env, cache_key, None) + if cached is not None: + return int(cached) + + object_cfg.resolve(env.scene) + geom_ids = env.scene[object_cfg.name].indexing.geom_ids[object_cfg.geom_ids] + geom_id = int(geom_ids[0].item()) + setattr(env, cache_key, geom_id) + return geom_id + + +def _resolve_mesh_id(env: ManagerBasedRlEnv, mesh_name: str) -> int: + cache_key = f"_dex_manip_mesh_id::{mesh_name}" + cached = getattr(env, cache_key, None) + if cached is not None: + return int(cached) + + available_names = [mesh.name for mesh in env.scene.spec.meshes if mesh.name is not None] + exact_name_to_id = {name: idx for idx, name in enumerate(available_names)} + + if mesh_name in exact_name_to_id: + mesh_id = exact_name_to_id[mesh_name] + else: + suffix_matches = [ + idx + for idx, full_name in enumerate(available_names) + if full_name.rsplit("/", 1)[-1] == mesh_name + ] + if len(suffix_matches) == 1: + mesh_id = suffix_matches[0] + elif len(suffix_matches) > 1: + matched = [available_names[idx] for idx in suffix_matches] + raise ValueError( + f"Mesh name {mesh_name!r} is ambiguous. Matches: {matched}. " + "Use a fully qualified mesh name." + ) + else: + raise ValueError( + f"Unknown mesh name {mesh_name!r}. Available: {sorted(available_names)}" + ) + + setattr(env, cache_key, mesh_id) + return mesh_id + + +def _object_dataid_per_env( + env: ManagerBasedRlEnv, + object_cfg: SceneEntityCfg, +) -> torch.Tensor: + geom_id = _resolve_object_geom_id(env, object_cfg) + dataid = env.sim.model.geom_dataid + if dataid.ndim == 2: + return dataid[:, geom_id].to(dtype=torch.int64, device=env.device) + value = int(dataid[geom_id].item()) + return torch.full((env.num_envs,), value, dtype=torch.int64, device=env.device) + + +def reward_mean(env: ManagerBasedRlEnv) -> torch.Tensor: + return env.reward_buf + + +def reward_for_mesh( + env: ManagerBasedRlEnv, + mesh_name: str, + object_cfg: SceneEntityCfg = _DEFAULT_OBJECT_CFG, +) -> torch.Tensor: + per_env_dataid = _object_dataid_per_env(env, object_cfg) + mesh_id = _resolve_mesh_id(env, mesh_name) + mask = per_env_dataid == mesh_id + if not torch.any(mask): + value = torch.tensor(0.0, device=env.device, dtype=env.reward_buf.dtype) + else: + value = env.reward_buf[mask].mean() + return torch.full_like(env.reward_buf, value) + + +def object_linear_speed( + env: ManagerBasedRlEnv, + asset_cfg: SceneEntityCfg = _DEFAULT_OBJECT_BODY_CFG, +) -> torch.Tensor: + asset: Entity = env.scene[asset_cfg.name] + speed = torch.linalg.vector_norm(asset.data.root_link_lin_vel_w, dim=-1) + return sanitize_to_range(speed, 0.0, 1e6, nan_default=0.0) + + +class object_rotation_progress: + def __init__(self, cfg, env: ManagerBasedRlEnv): + self._asset_cfg: SceneEntityCfg = cfg.params.get("asset_cfg", SceneEntityCfg("object")) + self._asset: Entity = env.scene[self._asset_cfg.name] + self._init_pos_w = torch.zeros((env.num_envs, 3), device=env.device) + self._init_roll = torch.zeros(env.num_envs, device=env.device) + self._init_pitch = torch.zeros(env.num_envs, device=env.device) + self._has_init = torch.zeros(env.num_envs, device=env.device, dtype=torch.bool) + + def reset(self, env_ids: torch.Tensor | slice | None = None): + if env_ids is None: + env_ids = slice(None) + pos_w = self._asset.data.root_link_pos_w + roll, pitch, _ = euler_xyz_from_quat(self._asset.data.root_link_quat_w) + self._init_pos_w[env_ids] = pos_w[env_ids] + self._init_roll[env_ids] = roll[env_ids] + self._init_pitch[env_ids] = pitch[env_ids] + self._has_init[env_ids] = True + + def __call__( + self, + env: ManagerBasedRlEnv, + asset_cfg: SceneEntityCfg = _DEFAULT_OBJECT_BODY_CFG, + target_yaw_rate: float = 0.20, + position_threshold: float = 0.02, + tilt_threshold: float = 0.35, + ) -> torch.Tensor: + del env, asset_cfg + yaw_rate = -self._asset.data.root_link_ang_vel_w[:, 2] + yaw_score = torch.clamp(yaw_rate / max(target_yaw_rate, 1e-6), min=0.0, max=1.0) + + pos_w = self._asset.data.root_link_pos_w + roll, pitch, _ = euler_xyz_from_quat(self._asset.data.root_link_quat_w) + pos_error = torch.linalg.vector_norm(pos_w - self._init_pos_w, dim=-1) + roll_error = wrap_to_pi(roll - self._init_roll).abs() + pitch_error = wrap_to_pi(pitch - self._init_pitch).abs() + tilt_error = torch.linalg.vector_norm(torch.stack([roll_error, pitch_error], dim=-1), dim=-1) + + pos_score = torch.clamp(1.0 - pos_error / max(position_threshold, 1e-6), min=0.0, max=1.0) + tilt_score = torch.clamp(1.0 - tilt_error / max(tilt_threshold, 1e-6), min=0.0, max=1.0) + progress = sanitize_to_range(yaw_score * pos_score * tilt_score, 0.0, 1.0, nan_default=0.0) + return torch.where(self._has_init, progress, torch.zeros_like(progress)) + + +class object_rotation_success: + def __init__(self, cfg, env: ManagerBasedRlEnv): + self._asset_cfg: SceneEntityCfg = cfg.params.get("asset_cfg", SceneEntityCfg("object")) + self._asset: Entity = env.scene[self._asset_cfg.name] + self._init_pos_w = torch.zeros((env.num_envs, 3), device=env.device) + self._init_roll = torch.zeros(env.num_envs, device=env.device) + self._init_pitch = torch.zeros(env.num_envs, device=env.device) + self._has_init = torch.zeros(env.num_envs, device=env.device, dtype=torch.bool) + + def reset(self, env_ids: torch.Tensor | slice | None = None): + if env_ids is None: + env_ids = slice(None) + pos_w = self._asset.data.root_link_pos_w + roll, pitch, _ = euler_xyz_from_quat(self._asset.data.root_link_quat_w) + self._init_pos_w[env_ids] = pos_w[env_ids] + self._init_roll[env_ids] = roll[env_ids] + self._init_pitch[env_ids] = pitch[env_ids] + self._has_init[env_ids] = True + + def __call__( + self, + env: ManagerBasedRlEnv, + asset_cfg: SceneEntityCfg = _DEFAULT_OBJECT_BODY_CFG, + target_yaw_rate: float = 0.20, + position_threshold: float = 0.02, + tilt_threshold: float = 0.35, + ) -> torch.Tensor: + del env, asset_cfg + yaw_rate = -self._asset.data.root_link_ang_vel_w[:, 2] + yaw_ok = yaw_rate >= target_yaw_rate + + pos_w = self._asset.data.root_link_pos_w + roll, pitch, _ = euler_xyz_from_quat(self._asset.data.root_link_quat_w) + pos_error = torch.linalg.vector_norm(pos_w - self._init_pos_w, dim=-1) + roll_error = wrap_to_pi(roll - self._init_roll).abs() + pitch_error = wrap_to_pi(pitch - self._init_pitch).abs() + tilt_error = torch.linalg.vector_norm(torch.stack([roll_error, pitch_error], dim=-1), dim=-1) + + stable = (pos_error <= position_threshold) & (tilt_error <= tilt_threshold) + success = self._has_init & yaw_ok & stable + return success.float() + + +class object_pose_rp_error_from_reset: + def __init__(self, cfg, env: ManagerBasedRlEnv): + self._asset_cfg: SceneEntityCfg = cfg.params.get("asset_cfg", SceneEntityCfg("object")) + self._component = cast(Literal["position", "tilt"], cfg.params.get("component", "position")) + if self._component not in ("position", "tilt"): + raise ValueError( + f"Unknown component '{self._component}'. Expected 'position' or 'tilt'." + ) + self._asset: Entity = env.scene[self._asset_cfg.name] + self._init_pos_w = torch.zeros((env.num_envs, 3), device=env.device) + self._init_roll = torch.zeros(env.num_envs, device=env.device) + self._init_pitch = torch.zeros(env.num_envs, device=env.device) + self._has_init = torch.zeros(env.num_envs, device=env.device, dtype=torch.bool) + + def reset(self, env_ids: torch.Tensor | slice | None = None): + if env_ids is None: + env_ids = slice(None) + pos_w = self._asset.data.root_link_pos_w + roll, pitch, _ = euler_xyz_from_quat(self._asset.data.root_link_quat_w) + self._init_pos_w[env_ids] = pos_w[env_ids] + self._init_roll[env_ids] = roll[env_ids] + self._init_pitch[env_ids] = pitch[env_ids] + self._has_init[env_ids] = True + + def __call__( + self, + env: ManagerBasedRlEnv, + component: Literal["position", "tilt"] = "position", + asset_cfg: SceneEntityCfg = _DEFAULT_OBJECT_BODY_CFG, + ) -> torch.Tensor: + del env, component, asset_cfg + pos_w = self._asset.data.root_link_pos_w + roll, pitch, _ = euler_xyz_from_quat(self._asset.data.root_link_quat_w) + pos_error = torch.linalg.vector_norm(pos_w - self._init_pos_w, dim=-1) + roll_error = wrap_to_pi(roll - self._init_roll).abs() + pitch_error = wrap_to_pi(pitch - self._init_pitch).abs() + tilt_error = torch.linalg.vector_norm(torch.stack([roll_error, pitch_error], dim=-1), dim=-1) + + metric = pos_error if self._component == "position" else tilt_error + metric = sanitize_to_range(metric, 0.0, 1e6, nan_default=0.0) + return torch.where(self._has_init, metric, torch.zeros_like(metric)) diff --git a/src/mjlab/tasks/dex_manip/objects.py b/src/mjlab/tasks/dex_manip/objects.py new file mode 100644 index 000000000..c846692e6 --- /dev/null +++ b/src/mjlab/tasks/dex_manip/objects.py @@ -0,0 +1,69 @@ +from __future__ import annotations + +import re +from pathlib import Path +from typing import Final + +DEFAULT_DEX_OBJECTS: Final[tuple[str, ...]] = ( + "water-bottle", + "orange", + "tuna-fish-can", +) + +_OBJECT_TO_MESH_NAME: Final[dict[str, str]] = { + "water-bottle": "water_bottle_mesh", + "orange": "orange_mesh", + "tuna-fish-can": "tuna_fish_can_mesh", +} + +_ASSETS_ROOT = Path(__file__).resolve().parent / "assets" / "objects" / "meshes" +_OBJECT_TO_MESH_FILE: Final[dict[str, Path]] = { + "water-bottle": _ASSETS_ROOT / "contactdb_objects" / "water_bottle.stl", + "orange": _ASSETS_ROOT / "ycb_objects" / "orange.stl", + "tuna-fish-can": _ASSETS_ROOT / "ycb_objects" / "tuna_fish_can.stl", +} + + +def available_object_names() -> tuple[str, ...]: + return tuple(_OBJECT_TO_MESH_NAME.keys()) + + +def parse_object_selection(raw: str | None) -> tuple[str, ...]: + if raw is None or raw.strip() == "" or raw.strip().lower() == "all": + return DEFAULT_DEX_OBJECTS + + tokens = [t.strip().lower() for t in re.split(r"[;,\s]+", raw) if t.strip()] + if not tokens: + raise ValueError("Object selection is empty.") + + normalized = tuple(token.replace("_", "-") for token in tokens) + unknown = [name for name in normalized if name not in _OBJECT_TO_MESH_NAME] + if unknown: + raise ValueError( + f"Unknown objects: {unknown}. Available: {sorted(_OBJECT_TO_MESH_NAME.keys())}" + ) + return normalized + + +def object_names_to_mesh_names(object_names: tuple[str, ...]) -> tuple[str, ...]: + unknown = [name for name in object_names if name not in _OBJECT_TO_MESH_NAME] + if unknown: + raise ValueError( + f"Unknown objects: {unknown}. Available: {sorted(_OBJECT_TO_MESH_NAME.keys())}" + ) + return tuple(_OBJECT_TO_MESH_NAME[name] for name in object_names) + + +def object_names_to_mesh_files(object_names: tuple[str, ...]) -> tuple[Path, ...]: + unknown = [name for name in object_names if name not in _OBJECT_TO_MESH_FILE] + if unknown: + raise ValueError( + f"Unknown objects: {unknown}. Available: {sorted(_OBJECT_TO_MESH_FILE.keys())}" + ) + missing = [name for name in object_names if not _OBJECT_TO_MESH_FILE[name].is_file()] + if missing: + raise FileNotFoundError( + "Missing mesh files for objects: " + f"{missing}. Expected under {_ASSETS_ROOT}." + ) + return tuple(_OBJECT_TO_MESH_FILE[name].resolve() for name in object_names) diff --git a/src/mjlab/tasks/dex_manip/rl_cfg.py b/src/mjlab/tasks/dex_manip/rl_cfg.py new file mode 100644 index 000000000..11395f8c7 --- /dev/null +++ b/src/mjlab/tasks/dex_manip/rl_cfg.py @@ -0,0 +1,42 @@ +from __future__ import annotations + +from mjlab.rl import RslRlModelCfg, RslRlOnPolicyRunnerCfg, RslRlPpoAlgorithmCfg + + +def dex_manip_ppo_runner_cfg() -> RslRlOnPolicyRunnerCfg: + return RslRlOnPolicyRunnerCfg( + actor=RslRlModelCfg( + hidden_dims=(512, 512, 256), + activation="elu", + obs_normalization=True, + distribution_cfg={ + "class_name": "GaussianDistribution", + "init_std": 0.7, + "std_type": "scalar", + }, + ), + critic=RslRlModelCfg( + hidden_dims=(512, 512, 256), + activation="elu", + obs_normalization=True, + ), + algorithm=RslRlPpoAlgorithmCfg( + value_loss_coef=1.0, + use_clipped_value_loss=True, + clip_param=0.2, + entropy_coef=0.003, + num_learning_epochs=5, + num_mini_batches=4, + learning_rate=1e-3, + schedule="adaptive", + gamma=0.99, + lam=0.95, + desired_kl=0.01, + max_grad_norm=1.0, + ), + experiment_name="dex_manip", + save_interval=100, + num_steps_per_env=32, + max_iterations=5_000, + clip_actions=1.0, + ) diff --git a/tests/test_dex_manip_task.py b/tests/test_dex_manip_task.py new file mode 100644 index 000000000..f385c57f1 --- /dev/null +++ b/tests/test_dex_manip_task.py @@ -0,0 +1,112 @@ +from __future__ import annotations + +import tempfile +from dataclasses import asdict + +import pytest +import torch +from conftest import get_test_device + +from mjlab.envs import ManagerBasedRlEnv +from mjlab.managers.scene_entity_config import SceneEntityCfg +from mjlab.rl import RslRlVecEnvWrapper +from mjlab.rl.runner import MjlabOnPolicyRunner +from mjlab.tasks.dex_manip.env_cfg import apply_dex_manip_overrides +from mjlab.tasks.registry import list_tasks, load_env_cfg, load_rl_cfg + + +@pytest.fixture(scope="module") +def device() -> str: + return get_test_device() + + +def _import_tasks() -> None: + import mjlab.tasks # noqa: F401 + + +def _resolve_object_geom_id(env: ManagerBasedRlEnv) -> int: + geom_cfg = SceneEntityCfg("object", geom_names=("object_geom",)) + geom_cfg.resolve(env.scene) + geom_ids = env.scene["object"].indexing.geom_ids[geom_cfg.geom_ids] + return int(geom_ids[0].item()) + + +def test_dex_manip_task_registered() -> None: + _import_tasks() + + assert "Mjlab-Dex-Manip" in list_tasks() + cfg = load_env_cfg("Mjlab-Dex-Manip") + + assert "randomize_object_mesh" in cfg.events + mesh_ids = cfg.events["randomize_object_mesh"].params["mesh_ids"] + assert mesh_ids == ( + "water_bottle_mesh", + "orange_mesh", + "tuna_fish_can_mesh", + ) + assert cfg.metrics is not None + assert "reward_mean" in cfg.metrics + assert "reward_water_bottle" in cfg.metrics + assert "reward_orange" in cfg.metrics + assert "reward_tuna_fish_can" in cfg.metrics + + +def test_dex_manip_apply_overrides() -> None: + _import_tasks() + + cfg = load_env_cfg("Mjlab-Dex-Manip") + selected = apply_dex_manip_overrides( + cfg, + objects="water-bottle,orange,tuna-fish-can", + envs_per_object=4, + assignment_mode="cycle", + ) + + assert selected == ("water-bottle", "orange", "tuna-fish-can") + assert cfg.scene.num_envs == 12 + assert cfg.events["randomize_object_mesh"].params["mesh_ids"] == ( + "water_bottle_mesh", + "orange_mesh", + "tuna_fish_can_mesh", + ) + + +@pytest.mark.slow +def test_dex_manip_training_smoke(device: str) -> None: + _import_tasks() + + env_cfg = load_env_cfg("Mjlab-Dex-Manip") + apply_dex_manip_overrides( + env_cfg, + objects="water-bottle,orange,tuna-fish-can", + envs_per_object=1, + assignment_mode="cycle", + ) + env_cfg.episode_length_s = 0.2 + + rl_cfg = asdict(load_rl_cfg("Mjlab-Dex-Manip")) + rl_cfg["max_iterations"] = 1 + rl_cfg["num_steps_per_env"] = 4 + rl_cfg["save_interval"] = 100 + + env = ManagerBasedRlEnv(cfg=env_cfg, device=device) + try: + geom_id = _resolve_object_geom_id(env) + if env.sim.model.geom_dataid.ndim != 2: + pytest.skip( + "Requires per-world geom_dataid support (MuJoCo-Warp PR #1191 or newer)." + ) + + env.reset() + values_before = env.sim.model.geom_dataid[:, geom_id].clone() + assert torch.unique(values_before).numel() >= 2 + + with tempfile.TemporaryDirectory() as tmpdir: + wrapped = RslRlVecEnvWrapper(env) + runner = MjlabOnPolicyRunner(wrapped, rl_cfg, log_dir=tmpdir, device=device) + runner.learn(num_learning_iterations=1, init_at_random_ep_len=False) + + values_after = env.sim.model.geom_dataid[:, geom_id] + assert torch.unique(values_after).numel() >= 2 + finally: + env.close() diff --git a/tests/test_geom_dataid_randomization.py b/tests/test_geom_dataid_randomization.py new file mode 100644 index 000000000..c8652f6a5 --- /dev/null +++ b/tests/test_geom_dataid_randomization.py @@ -0,0 +1,202 @@ +"""Tests for per-world mesh randomization via ``dr.geom_dataid``.""" + +import tempfile +from dataclasses import asdict + +import mujoco +import pytest +import torch +from conftest import get_test_device + +from mjlab.actuator import XmlMotorActuatorCfg +from mjlab.entity import EntityArticulationInfoCfg, EntityCfg +from mjlab.envs import ManagerBasedRlEnv, ManagerBasedRlEnvCfg, mdp +from mjlab.envs.mdp import dr +from mjlab.managers.event_manager import EventTermCfg +from mjlab.managers.observation_manager import ObservationGroupCfg, ObservationTermCfg +from mjlab.managers.scene_entity_config import SceneEntityCfg +from mjlab.rl import RslRlOnPolicyRunnerCfg, RslRlVecEnvWrapper +from mjlab.rl.runner import MjlabOnPolicyRunner +from mjlab.scene import SceneCfg +from mjlab.sim import MujocoCfg, SimulationCfg +from mjlab.terrains import TerrainEntityCfg + +MESH_VARIANT_XML = """ + + + + + + + + + + + + + + + + + + + + +""" + + +@pytest.fixture(scope="module") +def device(): + return get_test_device() + + +def _make_env(device: str, *, num_envs: int, with_reset_event: bool) -> ManagerBasedRlEnv: + robot_cfg = EntityCfg( + spec_fn=lambda: mujoco.MjSpec.from_string(MESH_VARIANT_XML), + articulation=EntityArticulationInfoCfg( + actuators=(XmlMotorActuatorCfg(target_names_expr=(".*",)),) + ), + ) + + events: dict[str, EventTermCfg] = {} + if with_reset_event: + events["randomize_object_mesh"] = EventTermCfg( + mode="reset", + func=dr.geom_dataid, + params={ + "mesh_ids": ("m_small", "m_medium", "m_large"), + "assignment_mode": "cycle", + "shared_random": True, + "asset_cfg": SceneEntityCfg("robot", geom_names=("obj_geom",)), + }, + ) + + env_cfg = ManagerBasedRlEnvCfg( + scene=SceneCfg( + terrain=TerrainEntityCfg(terrain_type="plane"), + num_envs=num_envs, + extent=1.0, + entities={"robot": robot_cfg}, + ), + observations={ + "actor": ObservationGroupCfg( + terms={ + "joint_pos": ObservationTermCfg( + func=lambda env: env.scene["robot"].data.joint_pos + ), + }, + ), + "critic": ObservationGroupCfg( + terms={ + "joint_pos": ObservationTermCfg( + func=lambda env: env.scene["robot"].data.joint_pos + ), + }, + ), + }, + actions={ + "joint_pos": mdp.JointPositionActionCfg( + entity_name="robot", actuator_names=(".*",), scale=1.0 + ) + }, + events=events, + sim=SimulationCfg(mujoco=MujocoCfg(timestep=0.01, iterations=1)), + decimation=1, + episode_length_s=0.1, + ) + return ManagerBasedRlEnv(cfg=env_cfg, device=device) + + +def _obj_geom_global_id(env: ManagerBasedRlEnv) -> int: + geom_cfg = SceneEntityCfg("robot", geom_names=("obj_geom",)) + geom_cfg.resolve(env.scene) + geom_ids = env.scene["robot"].indexing.geom_ids[geom_cfg.geom_ids] + return int(geom_ids[0].item()) + + +def _mesh_support_or_skip(env: ManagerBasedRlEnv) -> None: + if env.sim.model.geom_dataid.ndim != 2: + pytest.skip( + "This test requires per-world geom_dataid support from newer MuJoCo-Warp." + ) + + +def test_geom_dataid_randomizes_per_world(device): + torch.manual_seed(0) + env = _make_env(device, num_envs=12, with_reset_event=False) + try: + _mesh_support_or_skip(env) + geom_id = _obj_geom_global_id(env) + + dr.geom_dataid( + env, + env_ids=None, + mesh_ids=("m_small", "m_medium", "m_large"), + asset_cfg=SceneEntityCfg("robot", geom_names=("obj_geom",)), + assignment_mode="sample", + shared_random=True, + ) + + values = env.sim.model.geom_dataid[:, geom_id] + values_cpu = values.cpu() + assert values_cpu.shape == (env.num_envs,) + assert values_cpu.min().item() >= -1 + assert set(values_cpu.tolist()).issubset({0, 1, 2}) + assert torch.unique(values_cpu).numel() >= 2 + finally: + env.close() + + +def test_geom_dataid_cycle_assignment(device): + env = _make_env(device, num_envs=6, with_reset_event=False) + try: + _mesh_support_or_skip(env) + geom_id = _obj_geom_global_id(env) + + dr.geom_dataid( + env, + env_ids=None, + mesh_ids=("m_small", "m_medium", "m_large"), + asset_cfg=SceneEntityCfg("robot", geom_names=("obj_geom",)), + assignment_mode="cycle", + shared_random=True, + ) + values = env.sim.model.geom_dataid[:, geom_id].cpu() + assert values.tolist() == [0, 1, 2, 0, 1, 2] + finally: + env.close() + + +@pytest.mark.slow +def test_multi_mesh_rl_training_smoke(device): + env = _make_env(device, num_envs=6, with_reset_event=True) + try: + _mesh_support_or_skip(env) + env.reset() + geom_id = _obj_geom_global_id(env) + initial = env.sim.model.geom_dataid[:, geom_id].clone() + assert torch.unique(initial).numel() >= 2 + + wrapped_env = RslRlVecEnvWrapper(env) + agent_cfg = asdict( + RslRlOnPolicyRunnerCfg( + num_steps_per_env=4, + max_iterations=2, + save_interval=50, + ) + ) + + with tempfile.TemporaryDirectory() as tmpdir: + runner = MjlabOnPolicyRunner( + wrapped_env, agent_cfg, log_dir=tmpdir, device=device + ) + runner.learn(num_learning_iterations=2, init_at_random_ep_len=False) + + after = env.sim.model.geom_dataid[:, geom_id] + assert after.shape[0] == env.num_envs + assert torch.unique(after).numel() >= 2 + finally: + env.close() diff --git a/uv.lock b/uv.lock index eb44a9bdd..05b7db045 100644 --- a/uv.lock +++ b/uv.lock @@ -1677,7 +1677,7 @@ requires-dist = [ { name = "imageio-ffmpeg" }, { name = "mediapy", specifier = ">=1.2.6" }, { name = "mujoco", specifier = ">=3.6.0", index = "https://py.mujoco.org/" }, - { name = "mujoco-warp", git = "https://github.com/google-deepmind/mujoco_warp?rev=d9bcc326544b845111eed21079bfa8f8738e2518" }, + { name = "mujoco-warp", git = "https://github.com/google-deepmind/mujoco_warp?rev=e1b9d2640884801497ab0a3257145fd1e65d095b" }, { name = "onnxscript", specifier = ">=0.5.4" }, { name = "prettytable" }, { name = "rsl-rl-lib", specifier = "==5.0.1" }, @@ -1884,7 +1884,7 @@ wheels = [ [[package]] name = "mujoco-warp" version = "3.6.0" -source = { git = "https://github.com/google-deepmind/mujoco_warp?rev=d9bcc326544b845111eed21079bfa8f8738e2518#d9bcc326544b845111eed21079bfa8f8738e2518" } +source = { git = "https://github.com/google-deepmind/mujoco_warp?rev=e1b9d2640884801497ab0a3257145fd1e65d095b#e1b9d2640884801497ab0a3257145fd1e65d095b" } dependencies = [ { name = "absl-py" }, { name = "etils", extra = ["epath"] }, From c0e657ffe074c65a50f42713088df96a14549921 Mon Sep 17 00:00:00 2001 From: Andrew Rosemberg Date: Sat, 14 Mar 2026 01:37:17 -0400 Subject: [PATCH 2/3] fix rendering multiobject and object position --- .gitignore | 3 + scripts/demos/dex_manip.py | 250 ++++++++++++++++++++++--- src/mjlab/envs/manager_based_rl_env.py | 5 +- src/mjlab/envs/mdp/dr/geom.py | 2 +- src/mjlab/tasks/dex_manip/env_cfg.py | 33 +++- src/mjlab/tasks/dex_manip/inference.py | 136 ++++++++++++++ src/mjlab/tasks/dex_manip/metrics.py | 37 +--- src/mjlab/tasks/dex_manip/objects.py | 40 +++- src/mjlab/viewer/offscreen_renderer.py | 35 +++- tests/test_dex_manip_inference.py | 107 +++++++++++ tests/test_dex_manip_task.py | 14 ++ tests/test_offscreen_renderer.py | 33 ++++ 12 files changed, 626 insertions(+), 69 deletions(-) create mode 100644 src/mjlab/tasks/dex_manip/inference.py create mode 100644 tests/test_dex_manip_inference.py create mode 100644 tests/test_offscreen_renderer.py diff --git a/.gitignore b/.gitignore index 6aa942c59..541faa93a 100644 --- a/.gitignore +++ b/.gitignore @@ -14,7 +14,10 @@ artifacts/ render_robots.py benchmark_results/ *.sbatch +assets/checkpoints/ +assets/demos/ # Documentation outputs. **/_build/* **/generated/* +assets/checkpoints/ diff --git a/scripts/demos/dex_manip.py b/scripts/demos/dex_manip.py index bb5fb6f13..8f0eb33ac 100644 --- a/scripts/demos/dex_manip.py +++ b/scripts/demos/dex_manip.py @@ -1,7 +1,9 @@ -"""Record dexterous-manipulation demo videos across multiple object tasks. +"""Record dexterous-manipulation demo videos for one policy or a teacher bank. -This script replays one trained policy checkpoint on `Mjlab-Dex-Manip` and -records one MP4 per requested object. +Single-policy mode replays one checkpoint and records one MP4 per requested +object. Teacher-bank mode loads one frozen policy per object, steps all objects +inside one vectorized environment, and records one combined MP4 to demonstrate +batched multi-policy inference. Examples: # Use latest checkpoint from a run directory and record all matching objects. @@ -12,13 +14,24 @@ uv run python scripts/demos/dex_manip.py \ --checkpoint-file logs/rsl_rl/.../model_4000.pt \ --objects water-bottle,orange,tuna-fish-can + + # Use a frozen teacher bank and record one combined multi-object video. + uv run python scripts/demos/dex_manip.py \ + --policy-bank-file assets/checkpoints/dex_manip_teacher_bank/legacy_20260228/policy_bank.json \ + --objects water-bottle,orange,tuna-fish-can """ from __future__ import annotations +import json +import os import re from dataclasses import asdict, dataclass from pathlib import Path +from typing import Mapping + +os.environ.setdefault("MUJOCO_GL", "egl") +os.environ.setdefault("PYOPENGL_PLATFORM", "egl") import torch import tyro @@ -27,16 +40,22 @@ from mjlab.envs import ManagerBasedRlEnv from mjlab.rl import MjlabOnPolicyRunner, RslRlVecEnvWrapper from mjlab.tasks.dex_manip.env_cfg import apply_dex_manip_overrides +from mjlab.tasks.dex_manip.inference import FrozenPolicyBank, object_policy_ids_from_env from mjlab.tasks.dex_manip.objects import parse_object_selection from mjlab.tasks.registry import load_env_cfg, load_rl_cfg, load_runner_cls from mjlab.utils.torch import configure_torch_backends from mjlab.utils.wrappers import VideoRecorder +from mjlab.viewer import ViewerConfig @dataclass(frozen=True) class DexManipDemoConfig: checkpoint_file: str | None = None run_dir: str | None = None + policy_bank: str | None = None + """Semicolon-separated mapping: object=checkpoint_or_run_dir;...""" + policy_bank_file: str | None = None + """JSON mapping of object -> checkpoint path or run directory.""" task_id: str = "Mjlab-Dex-Manip" objects: str = "all" """Comma/semicolon/space separated object names, or 'all'.""" @@ -55,17 +74,12 @@ def _checkpoint_step(path: Path) -> int: return int(match.group(1)) -def _resolve_checkpoint(cfg: DexManipDemoConfig) -> Path: - if cfg.checkpoint_file is not None: - checkpoint = Path(cfg.checkpoint_file).expanduser().resolve() - if not checkpoint.is_file(): - raise FileNotFoundError(f"Checkpoint file not found: {checkpoint}") - return checkpoint - - if cfg.run_dir is None: - raise ValueError("Pass either --checkpoint-file or --run-dir.") +def _resolve_checkpoint_from_path(raw_path: str) -> Path: + path = Path(raw_path).expanduser().resolve() + if path.is_file(): + return path - run_dir = Path(cfg.run_dir).expanduser().resolve() + run_dir = path if not run_dir.is_dir(): raise FileNotFoundError(f"Run dir not found: {run_dir}") @@ -76,10 +90,91 @@ def _resolve_checkpoint(cfg: DexManipDemoConfig) -> Path: return max(candidates, key=lambda path: (_checkpoint_step(path), path.stat().st_mtime)) +def _resolve_checkpoint(cfg: DexManipDemoConfig) -> Path: + if cfg.checkpoint_file is not None: + checkpoint = _resolve_checkpoint_from_path(cfg.checkpoint_file) + if not checkpoint.is_file(): + raise FileNotFoundError(f"Checkpoint file not found: {checkpoint}") + return checkpoint + + if cfg.run_dir is None: + raise ValueError( + "Pass either --checkpoint-file/--run-dir for single-policy mode, " + "or --policy-bank/--policy-bank-file for teacher-bank mode." + ) + return _resolve_checkpoint_from_path(cfg.run_dir) + + def _resolve_objects(objects: str) -> list[str]: return list(parse_object_selection(objects)) +def _parse_policy_bank_inline(raw: str) -> dict[str, str]: + mapping: dict[str, str] = {} + for item in [token.strip() for token in raw.split(";") if token.strip()]: + if "=" not in item: + raise ValueError( + f"Invalid policy bank item {item!r}. Expected 'object=checkpoint_or_run_dir'." + ) + object_name, path = item.split("=", 1) + normalized = parse_object_selection(object_name)[0] + mapping[normalized] = path.strip() + return mapping + + +def _resolve_policy_bank(cfg: DexManipDemoConfig) -> tuple[str, dict[str, Path]] | None: + raw_mapping: dict[str, str] | None = None + if cfg.policy_bank is not None: + raw_mapping = _parse_policy_bank_inline(cfg.policy_bank) + elif cfg.policy_bank_file is not None: + bank_path = Path(cfg.policy_bank_file).expanduser().resolve() + if not bank_path.is_file(): + raise FileNotFoundError(f"Policy bank file not found: {bank_path}") + raw_mapping = json.loads(bank_path.read_text()) + if not isinstance(raw_mapping, dict): + raise ValueError(f"Policy bank file must contain an object mapping: {bank_path}") + + if raw_mapping is None: + return None + + objects = _resolve_objects(cfg.objects) + missing = [name for name in objects if name not in raw_mapping] + if missing: + raise ValueError(f"Policy bank is missing objects {missing}. Available: {sorted(raw_mapping)}") + + resolved = {name: _resolve_checkpoint_from_path(str(raw_mapping[name])) for name in objects} + return ",".join(objects), resolved + + +def _make_runner( + task_id: str, + env: RslRlVecEnvWrapper, + checkpoint: Path, + device: str, +): + agent_cfg = load_rl_cfg(task_id) + runner_cls = load_runner_cls(task_id) or MjlabOnPolicyRunner + runner = runner_cls(env, asdict(agent_cfg), device=device) + runner.load(str(checkpoint), load_cfg={"actor": True}, strict=True, map_location=device) + return runner + + +def _configure_demo_playback_cfg( + env_cfg, + *, + steps: int, + video_height: int | None, + video_width: int | None, +) -> None: + del steps + env_cfg.episode_length_s = 1e9 + env_cfg.terminations = {"nan": env_cfg.terminations["nan"]} + if video_height is not None: + env_cfg.viewer.height = video_height + if video_width is not None: + env_cfg.viewer.width = video_width + + def _record_one_task( task_id: str, object_name: str, @@ -100,11 +195,12 @@ def _record_one_task( envs_per_object=num_envs, assignment_mode="cycle", ) - - if video_height is not None: - env_cfg.viewer.height = video_height - if video_width is not None: - env_cfg.viewer.width = video_width + _configure_demo_playback_cfg( + env_cfg, + steps=steps, + video_height=video_height, + video_width=video_width, + ) name_prefix = f"{object_name}__{checkpoint.stem}" expected_video_path = out_dir / f"{name_prefix}-step-0.mp4" @@ -120,9 +216,7 @@ def _record_one_task( ) env = RslRlVecEnvWrapper(env, clip_actions=agent_cfg.clip_actions) - runner_cls = load_runner_cls(task_id) or MjlabOnPolicyRunner - runner = runner_cls(env, asdict(agent_cfg), device=device) - runner.load(str(checkpoint), load_cfg={"actor": True}, strict=True, map_location=device) + runner = _make_runner(task_id, env, checkpoint, device) policy = runner.get_inference_policy(device=device) policy.eval() @@ -136,6 +230,90 @@ def _record_one_task( return expected_video_path +def _record_policy_bank( + task_id: str, + checkpoints: Mapping[str, Path], + out_dir: Path, + device: str, + steps: int, + num_envs: int, + video_height: int | None, + video_width: int | None, +) -> Path: + object_names = list(checkpoints) + env_cfg = load_env_cfg(task_id, play=True) + agent_cfg = load_rl_cfg(task_id) + + apply_dex_manip_overrides( + env_cfg, + objects=";".join(object_names), + envs_per_object=num_envs, + assignment_mode="cycle", + ) + + env_origins = [] + env_spacing = float(env_cfg.scene.env_spacing) + total_envs = len(object_names) * num_envs + if total_envs > 1: + center_index = 0.5 * (total_envs - 1) + env_origins = [((index - center_index) * env_spacing, 0.0, 0.0) for index in range(total_envs)] + else: + env_origins = [(0.0, 0.0, 0.0)] + x_coords = [origin[0] for origin in env_origins] + center_x = 0.5 * (min(x_coords) + max(x_coords)) + half_span_x = 0.5 * (max(x_coords) - min(x_coords)) + env_cfg.viewer.origin_type = ViewerConfig.OriginType.WORLD + env_cfg.viewer.lookat = (center_x, 0.0, 0.22) + env_cfg.viewer.distance = max(1.2, 1.2 + 0.9 * half_span_x) + env_cfg.viewer.elevation = -18.0 + env_cfg.viewer.azimuth = 90.0 + _configure_demo_playback_cfg( + env_cfg, + steps=steps, + video_height=video_height, + video_width=video_width, + ) + + checkpoint_slug = "__".join( + f"{object_name}-{checkpoint.stem}" for object_name, checkpoint in checkpoints.items() + ) + name_prefix = f"teacher_bank__{checkpoint_slug}" + expected_video_path = out_dir / f"{name_prefix}-step-0.mp4" + + env = ManagerBasedRlEnv(cfg=env_cfg, device=device, render_mode="rgb_array") + env = VideoRecorder( + env, + video_folder=out_dir, + step_trigger=lambda step: step == 0, + video_length=steps, + disable_logger=True, + name_prefix=name_prefix, + ) + env = RslRlVecEnvWrapper(env, clip_actions=agent_cfg.clip_actions) + + runners = { + object_name: _make_runner(task_id, env, checkpoint, device) + for object_name, checkpoint in checkpoints.items() + } + policies = [runners[object_name].get_inference_policy(device=device).eval() for object_name in object_names] + bank = FrozenPolicyBank(object_names, policies) + + obs, _ = env.reset() + env_policy_ids = object_policy_ids_from_env(env.unwrapped, object_names) + counts = torch.bincount(env_policy_ids, minlength=len(object_names)).tolist() + print(f"[INFO] teacher_bank_env_counts={dict(zip(object_names, counts, strict=True))}") + + with torch.no_grad(): + for _ in range(steps): + action = bank(obs, env_policy_ids) + obs, _, dones, _ = env.step(action) + if torch.any(dones): + env_policy_ids = object_policy_ids_from_env(env.unwrapped, object_names) + + env.close() + return expected_video_path + + def main() -> None: cfg = tyro.cli(DexManipDemoConfig, config=mjlab.TYRO_FLAGS) configure_torch_backends() @@ -144,22 +322,22 @@ def main() -> None: import mjlab.tasks as _mjlab_tasks # noqa: F401 device = cfg.device or ("cuda:0" if torch.cuda.is_available() else "cpu") - checkpoint = _resolve_checkpoint(cfg) objects = _resolve_objects(cfg.objects) out_dir = Path(cfg.output_dir).expanduser().resolve() out_dir.mkdir(parents=True, exist_ok=True) - print(f"[INFO] checkpoint={checkpoint}") print(f"[INFO] device={device}") print(f"[INFO] task_id={cfg.task_id}") print(f"[INFO] objects={objects}") print(f"[INFO] output_dir={out_dir}") - for object_name in objects: - video_path = _record_one_task( + policy_bank = _resolve_policy_bank(cfg) + if policy_bank is not None: + _, checkpoints = policy_bank + print(f"[INFO] teacher_bank={checkpoints}") + video_path = _record_policy_bank( task_id=cfg.task_id, - object_name=object_name, - checkpoint=checkpoint, + checkpoints=checkpoints, out_dir=out_dir, device=device, steps=cfg.steps, @@ -167,7 +345,23 @@ def main() -> None: video_height=cfg.video_height, video_width=cfg.video_width, ) - print(f"[DONE] {object_name} -> {video_path}") + print(f"[DONE] teacher_bank -> {video_path}") + else: + checkpoint = _resolve_checkpoint(cfg) + print(f"[INFO] checkpoint={checkpoint}") + for object_name in objects: + video_path = _record_one_task( + task_id=cfg.task_id, + object_name=object_name, + checkpoint=checkpoint, + out_dir=out_dir, + device=device, + steps=cfg.steps, + num_envs=cfg.num_envs, + video_height=cfg.video_height, + video_width=cfg.video_width, + ) + print(f"[DONE] {object_name} -> {video_path}") if __name__ == "__main__": diff --git a/src/mjlab/envs/manager_based_rl_env.py b/src/mjlab/envs/manager_based_rl_env.py index 1eb49ca64..1cdda89a9 100644 --- a/src/mjlab/envs/manager_based_rl_env.py +++ b/src/mjlab/envs/manager_based_rl_env.py @@ -210,7 +210,10 @@ def __init__( self._offline_renderer: OffscreenRenderer | None = None if self.render_mode == "rgb_array": renderer = OffscreenRenderer( - model=self.sim.mj_model, cfg=self.cfg.viewer, scene=self.scene + model=self.sim.mj_model, + cfg=self.cfg.viewer, + scene=self.scene, + batched_model=self.sim.model, ) renderer.initialize() self._offline_renderer = renderer diff --git a/src/mjlab/envs/mdp/dr/geom.py b/src/mjlab/envs/mdp/dr/geom.py index 9deb062e5..37eb0845a 100644 --- a/src/mjlab/envs/mdp/dr/geom.py +++ b/src/mjlab/envs/mdp/dr/geom.py @@ -394,7 +394,7 @@ def geom_dataid( f"Expected geom_dataid.shape[0] == num_envs ({env.num_envs}), " f"got {model_field.shape[0]}." ) - if model_field.stride(0) == 0: + if env.num_envs > 1 and model_field.stride(0) == 0: raise ValueError( "geom_dataid appears to be a broadcasted view (stride(0)==0), so per-world " "writes would alias across environments. Ensure geom_dataid is expanded via " diff --git a/src/mjlab/tasks/dex_manip/env_cfg.py b/src/mjlab/tasks/dex_manip/env_cfg.py index 1de70fcb9..fef381994 100644 --- a/src/mjlab/tasks/dex_manip/env_cfg.py +++ b/src/mjlab/tasks/dex_manip/env_cfg.py @@ -56,6 +56,25 @@ def get_multi_object_spec() -> mujoco.MjSpec: LEAP_ACTION_SCALE = 1.0 / 24.0 PALM_CENTER_GEOM_EXPR = "palm_collision_.*" +GRASP_INIT_JOINT_POS = { + "if_mcp": 0.1, + "if_rot": 0.4, + "if_pip": 1.3, + "if_dip": 0.0, + "mf_mcp": 0.1, + "mf_rot": 0.0, + "mf_pip": 1.3, + "mf_dip": 0.0, + "rf_mcp": 0.1, + "rf_rot": -0.4, + "rf_pip": 1.3, + "rf_dip": 0.0, + "th_cmc": 1.45, + "th_axl": -1.5, + "th_mcp": 0.579, + "th_ipl": 1.37, +} +OBJECT_SPAWN_POS = (-0.092, 0.055, 0.27) def make_dex_manip_base_env_cfg() -> ManagerBasedRlEnvCfg: @@ -491,9 +510,19 @@ def dex_manip_env_cfg( envs_per_object: int | None = 8, ) -> ManagerBasedRlEnvCfg: cfg = make_dex_manip_base_env_cfg() + robot_cfg = get_leap_left_custom_hand_cfg() + robot_cfg.init_state.joint_pos = dict(GRASP_INIT_JOINT_POS) cfg.scene.entities = { - "robot": get_leap_left_custom_hand_cfg(), - "object": EntityCfg(spec_fn=get_multi_object_spec), + "robot": robot_cfg, + "object": EntityCfg( + init_state=EntityCfg.InitialStateCfg( + pos=OBJECT_SPAWN_POS, + rot=(1.0, 0.0, 0.0, 0.0), + lin_vel=(0.0, 0.0, 0.0), + ang_vel=(0.0, 0.0, 0.0), + ), + spec_fn=get_multi_object_spec, + ), } selected_objects = apply_dex_manip_overrides( diff --git a/src/mjlab/tasks/dex_manip/inference.py b/src/mjlab/tasks/dex_manip/inference.py new file mode 100644 index 000000000..053a8a062 --- /dev/null +++ b/src/mjlab/tasks/dex_manip/inference.py @@ -0,0 +1,136 @@ +from __future__ import annotations + +import copy +from collections.abc import Mapping, Sequence + +import torch +from torch import nn +from torch.func import functional_call, stack_module_state, vmap + +from mjlab.managers.scene_entity_config import SceneEntityCfg + +from .objects import object_names_to_mesh_names, resolve_mesh_id + +_DEFAULT_OBJECT_CFG = SceneEntityCfg("object", geom_names=("object_geom",)) + + +def _resolve_object_geom_id(env, object_cfg: SceneEntityCfg) -> int: + cache_key = f"_dex_manip_object_geom_id::{object_cfg.name}::{object_cfg.geom_names}" + cached = getattr(env, cache_key, None) + if cached is not None: + return int(cached) + + object_cfg.resolve(env.scene) + geom_ids = env.scene[object_cfg.name].indexing.geom_ids[object_cfg.geom_ids] + geom_id = int(geom_ids[0].item()) + setattr(env, cache_key, geom_id) + return geom_id + + +def object_policy_ids_from_env( + env, + object_names: Sequence[str], + object_cfg: SceneEntityCfg = _DEFAULT_OBJECT_CFG, +) -> torch.Tensor: + """Return per-env policy ids by reading the active object mesh assignment.""" + geom_id = _resolve_object_geom_id(env, object_cfg) + dataid = env.sim.model.geom_dataid + if dataid.ndim != 2: + raise ValueError( + "Expected per-world geom_dataid with shape (num_envs, ngeom). " + "Install MuJoCo-Warp with per-world geom_dataid support." + ) + + per_env_dataid = dataid[:, geom_id].to(dtype=torch.int64, device=env.device) + mesh_ids = torch.tensor( + [resolve_mesh_id(env, mesh_name) for mesh_name in object_names_to_mesh_names(tuple(object_names))], + device=env.device, + dtype=torch.int64, + ) + + matches = per_env_dataid.unsqueeze(-1) == mesh_ids.unsqueeze(0) + if not torch.all(matches.any(dim=-1)): + unknown = torch.unique(per_env_dataid[~matches.any(dim=-1)]).tolist() + raise ValueError( + f"Found envs assigned to mesh ids {unknown}, which are not covered by {tuple(object_names)}." + ) + return matches.to(dtype=torch.int64).argmax(dim=-1) + + +def _index_obs(obs: Mapping[str, torch.Tensor], index: torch.Tensor) -> dict[str, torch.Tensor]: + return {key: value[index] for key, value in obs.items()} + + +class FrozenPolicyBank(nn.Module): + """Batched frozen inference over multiple policies with equal env counts. + + The intended use is a single vectorized environment whose worlds are assigned + to different objects. Each policy sees only the worlds for its own object, but + all policy forwards are evaluated together inside one batched GPU call. + """ + + def __init__(self, policy_names: Sequence[str], policies: Sequence[nn.Module]): + super().__init__() + if not policies: + raise ValueError("FrozenPolicyBank requires at least one policy.") + if len(policy_names) != len(policies): + raise ValueError( + f"policy_names ({len(policy_names)}) and policies ({len(policies)}) must match." + ) + + self.policy_names = tuple(policy_names) + self.num_policies = len(self.policy_names) + + modules = [policy.eval() for policy in policies] + self._template = copy.deepcopy(modules[0]).to("meta") + self._params, self._buffers = stack_module_state(modules) + + def _call_single( + self, + params: Mapping[str, torch.Tensor], + buffers: Mapping[str, torch.Tensor], + obs: Mapping[str, torch.Tensor], + ) -> torch.Tensor: + return functional_call(self._template, (params, buffers), (obs,)) + + def forward(self, obs: Mapping[str, torch.Tensor], env_policy_ids: torch.Tensor) -> torch.Tensor: + if env_policy_ids.ndim != 1: + raise ValueError( + f"env_policy_ids must be 1-D with shape (num_envs,), got {tuple(env_policy_ids.shape)}." + ) + if env_policy_ids.numel() == 0: + raise ValueError("env_policy_ids is empty.") + + env_policy_ids = env_policy_ids.to(dtype=torch.long) + order = torch.argsort(env_policy_ids, stable=True) + sorted_policy_ids = env_policy_ids[order] + + unique_ids, counts = torch.unique_consecutive(sorted_policy_ids, return_counts=True) + expected_ids = torch.arange(self.num_policies, device=env_policy_ids.device) + if unique_ids.numel() != self.num_policies or not torch.equal(unique_ids, expected_ids): + raise ValueError( + "FrozenPolicyBank currently expects every policy id 0..N-1 to appear at least once. " + f"Got unique ids {unique_ids.tolist()}." + ) + if not torch.all(counts == counts[0]): + raise ValueError( + "FrozenPolicyBank currently expects equal env counts per policy. " + f"Got counts {counts.tolist()}." + ) + + envs_per_policy = int(counts[0].item()) + grouped_obs = { + key: value[order].reshape(self.num_policies, envs_per_policy, *value.shape[1:]) + for key, value in obs.items() + } + + grouped_actions = vmap(self._call_single, in_dims=(0, 0, 0))( + self._params, + self._buffers, + grouped_obs, + ) + sorted_actions = grouped_actions.reshape(-1, *grouped_actions.shape[2:]) + + inv_order = torch.empty_like(order) + inv_order[order] = torch.arange(order.numel(), device=order.device) + return sorted_actions[inv_order] diff --git a/src/mjlab/tasks/dex_manip/metrics.py b/src/mjlab/tasks/dex_manip/metrics.py index c9e244389..e8b232265 100644 --- a/src/mjlab/tasks/dex_manip/metrics.py +++ b/src/mjlab/tasks/dex_manip/metrics.py @@ -8,6 +8,7 @@ from mjlab.utils.lab_api.math import euler_xyz_from_quat, wrap_to_pi from .mdp.numerics import sanitize_to_range +from .objects import resolve_mesh_id if TYPE_CHECKING: from mjlab.entity import Entity @@ -34,40 +35,6 @@ def _resolve_object_geom_id( return geom_id -def _resolve_mesh_id(env: ManagerBasedRlEnv, mesh_name: str) -> int: - cache_key = f"_dex_manip_mesh_id::{mesh_name}" - cached = getattr(env, cache_key, None) - if cached is not None: - return int(cached) - - available_names = [mesh.name for mesh in env.scene.spec.meshes if mesh.name is not None] - exact_name_to_id = {name: idx for idx, name in enumerate(available_names)} - - if mesh_name in exact_name_to_id: - mesh_id = exact_name_to_id[mesh_name] - else: - suffix_matches = [ - idx - for idx, full_name in enumerate(available_names) - if full_name.rsplit("/", 1)[-1] == mesh_name - ] - if len(suffix_matches) == 1: - mesh_id = suffix_matches[0] - elif len(suffix_matches) > 1: - matched = [available_names[idx] for idx in suffix_matches] - raise ValueError( - f"Mesh name {mesh_name!r} is ambiguous. Matches: {matched}. " - "Use a fully qualified mesh name." - ) - else: - raise ValueError( - f"Unknown mesh name {mesh_name!r}. Available: {sorted(available_names)}" - ) - - setattr(env, cache_key, mesh_id) - return mesh_id - - def _object_dataid_per_env( env: ManagerBasedRlEnv, object_cfg: SceneEntityCfg, @@ -90,7 +57,7 @@ def reward_for_mesh( object_cfg: SceneEntityCfg = _DEFAULT_OBJECT_CFG, ) -> torch.Tensor: per_env_dataid = _object_dataid_per_env(env, object_cfg) - mesh_id = _resolve_mesh_id(env, mesh_name) + mesh_id = resolve_mesh_id(env, mesh_name) mask = per_env_dataid == mesh_id if not torch.any(mask): value = torch.tensor(0.0, device=env.device, dtype=env.reward_buf.dtype) diff --git a/src/mjlab/tasks/dex_manip/objects.py b/src/mjlab/tasks/dex_manip/objects.py index c846692e6..6a9ec4ed7 100644 --- a/src/mjlab/tasks/dex_manip/objects.py +++ b/src/mjlab/tasks/dex_manip/objects.py @@ -2,7 +2,10 @@ import re from pathlib import Path -from typing import Final +from typing import TYPE_CHECKING, Final + +if TYPE_CHECKING: + from mjlab.envs import ManagerBasedRlEnv DEFAULT_DEX_OBJECTS: Final[tuple[str, ...]] = ( "water-bottle", @@ -67,3 +70,38 @@ def object_names_to_mesh_files(object_names: tuple[str, ...]) -> tuple[Path, ... f"{missing}. Expected under {_ASSETS_ROOT}." ) return tuple(_OBJECT_TO_MESH_FILE[name].resolve() for name in object_names) + + +def resolve_mesh_id(env: "ManagerBasedRlEnv", mesh_name: str) -> int: + """Resolve a mesh id by full name or unique basename.""" + cache_key = f"_dex_manip_mesh_id::{mesh_name}" + cached = getattr(env, cache_key, None) + if cached is not None: + return int(cached) + + available_names = [mesh.name for mesh in env.scene.spec.meshes if mesh.name is not None] + exact_name_to_id = {name: idx for idx, name in enumerate(available_names)} + + if mesh_name in exact_name_to_id: + mesh_id = exact_name_to_id[mesh_name] + else: + suffix_matches = [ + idx + for idx, full_name in enumerate(available_names) + if full_name.rsplit("/", 1)[-1] == mesh_name + ] + if len(suffix_matches) == 1: + mesh_id = suffix_matches[0] + elif len(suffix_matches) > 1: + matched = [available_names[idx] for idx in suffix_matches] + raise ValueError( + f"Mesh name {mesh_name!r} is ambiguous. Matches: {matched}. " + "Use a fully qualified mesh name." + ) + else: + raise ValueError( + f"Unknown mesh name {mesh_name!r}. Available: {sorted(available_names)}" + ) + + setattr(env, cache_key, mesh_id) + return mesh_id diff --git a/src/mjlab/viewer/offscreen_renderer.py b/src/mjlab/viewer/offscreen_renderer.py index 4abad01cf..e20f18bf6 100644 --- a/src/mjlab/viewer/offscreen_renderer.py +++ b/src/mjlab/viewer/offscreen_renderer.py @@ -1,5 +1,6 @@ """MuJoCo offscreen renderer for headless visualization.""" +from typing import TYPE_CHECKING from typing import Any, Callable import mujoco @@ -9,13 +10,43 @@ from mjlab.viewer.native.visualizer import MujocoNativeDebugVisualizer from mjlab.viewer.viewer_config import ViewerConfig +if TYPE_CHECKING: + from mjlab.sim.sim import ModelBridge + + +def _copy_per_env_render_fields( + mj_model: mujoco.MjModel, + batched_model: "ModelBridge | None", + env_idx: int, +) -> None: + """Copy per-env render-relevant model fields into the CPU MuJoCo model. + + MuJoCo offscreen rendering operates on a single CPU ``MjModel`` / ``MjData``. + When simulation uses per-world expanded model fields (e.g. ``geom_dataid`` for + mixed meshes), we must copy the selected world's values into that CPU model + before ``mj_forward`` so rendering matches simulation. + """ + if batched_model is None: + return + + geom_dataid = getattr(batched_model, "geom_dataid", None) + if geom_dataid is not None and getattr(geom_dataid, "ndim", 0) == 2: + mj_model.geom_dataid[:] = geom_dataid[env_idx].cpu().numpy() + class OffscreenRenderer: - def __init__(self, model: mujoco.MjModel, cfg: ViewerConfig, scene: Scene) -> None: + def __init__( + self, + model: mujoco.MjModel, + cfg: ViewerConfig, + scene: Scene, + batched_model: "ModelBridge | None" = None, + ) -> None: self._cfg = cfg self._model = model self._data = mujoco.MjData(model) self._scene = scene + self._batched_model = batched_model self._orig_extent = float(self._model.stat.extent) self._render_extent = self._compute_render_extent() # Keep extent override local to offscreen rendering so shadow/camera scaling @@ -73,6 +104,7 @@ def update( return env_idx = max(0, min(int(self._cfg.env_idx), nworld - 1)) + _copy_per_env_render_fields(self._model, self._batched_model, env_idx) if self._model.nq > 0: self._data.qpos[:] = data.qpos[env_idx].cpu().numpy() self._data.qvel[:] = data.qvel[env_idx].cpu().numpy() @@ -93,6 +125,7 @@ def update( # Add nearest neighboring environments as geoms for context. for i in self._get_extra_env_ids(nworld, env_idx): + _copy_per_env_render_fields(self._model, self._batched_model, i) if self._model.nq > 0: self._data.qpos[:] = data.qpos[i].cpu().numpy() self._data.qvel[:] = data.qvel[i].cpu().numpy() diff --git a/tests/test_dex_manip_inference.py b/tests/test_dex_manip_inference.py new file mode 100644 index 000000000..b468f5809 --- /dev/null +++ b/tests/test_dex_manip_inference.py @@ -0,0 +1,107 @@ +from __future__ import annotations + +import pytest +import torch +from conftest import get_test_device + +from mjlab.envs import ManagerBasedRlEnv +from mjlab.tasks.dex_manip.env_cfg import apply_dex_manip_overrides +from mjlab.tasks.dex_manip.inference import FrozenPolicyBank, object_policy_ids_from_env +from mjlab.tasks.registry import load_env_cfg + + +class _AffinePolicy(torch.nn.Module): + def __init__(self, scale: float, bias: float): + super().__init__() + self.scale = torch.nn.Parameter(torch.tensor(scale, dtype=torch.float32)) + self.bias = torch.nn.Parameter(torch.tensor(bias, dtype=torch.float32)) + + def forward(self, obs): + return obs["actor"] * self.scale + self.bias + + +@pytest.fixture(scope="module") +def device() -> str: + return get_test_device() + + +def _import_tasks() -> None: + import mjlab.tasks # noqa: F401 + + +def test_frozen_policy_bank_matches_per_policy_dispatch() -> None: + policies = [ + _AffinePolicy(1.0, 0.5), + _AffinePolicy(2.0, -1.0), + _AffinePolicy(-3.0, 2.5), + ] + bank = FrozenPolicyBank(("water-bottle", "orange", "tuna-fish-can"), policies) + + obs = { + "actor": torch.tensor( + [ + [1.0, 10.0], + [2.0, 20.0], + [3.0, 30.0], + [4.0, 40.0], + [5.0, 50.0], + [6.0, 60.0], + ] + ) + } + env_policy_ids = torch.tensor([0, 1, 2, 0, 1, 2], dtype=torch.long) + + actual = bank(obs, env_policy_ids) + expected = torch.empty_like(obs["actor"]) + for policy_id, policy in enumerate(policies): + env_ids = torch.nonzero(env_policy_ids == policy_id, as_tuple=False).squeeze(-1) + expected[env_ids] = policy({"actor": obs["actor"][env_ids]}) + + torch.testing.assert_close(actual, expected) + + +def test_object_policy_ids_from_env_cycle_assignment(device: str) -> None: + _import_tasks() + + env_cfg = load_env_cfg("Mjlab-Dex-Manip") + apply_dex_manip_overrides( + env_cfg, + objects="water-bottle,orange,tuna-fish-can", + envs_per_object=2, + assignment_mode="cycle", + ) + + env = ManagerBasedRlEnv(cfg=env_cfg, device=device) + try: + if env.sim.model.geom_dataid.ndim != 2: + pytest.skip( + "Requires per-world geom_dataid support (MuJoCo-Warp PR #1191 or newer)." + ) + + env.reset() + policy_ids = object_policy_ids_from_env( + env, + ("water-bottle", "orange", "tuna-fish-can"), + ) + + assert policy_ids.tolist() == [0, 1, 2, 0, 1, 2] + finally: + env.close() + + +def test_single_env_object_assignment_reset(device: str) -> None: + _import_tasks() + + env_cfg = load_env_cfg("Mjlab-Dex-Manip") + apply_dex_manip_overrides( + env_cfg, + objects="water-bottle", + envs_per_object=1, + assignment_mode="cycle", + ) + + env = ManagerBasedRlEnv(cfg=env_cfg, device=device) + try: + env.reset() + finally: + env.close() diff --git a/tests/test_dex_manip_task.py b/tests/test_dex_manip_task.py index f385c57f1..8d52d4359 100644 --- a/tests/test_dex_manip_task.py +++ b/tests/test_dex_manip_task.py @@ -71,6 +71,20 @@ def test_dex_manip_apply_overrides() -> None: ) +def test_dex_manip_task_uses_task_specific_grasp_and_spawn() -> None: + _import_tasks() + + cfg = load_env_cfg("Mjlab-Dex-Manip") + + robot_cfg = cfg.scene.entities["robot"] + object_cfg = cfg.scene.entities["object"] + + assert robot_cfg.init_state.joint_pos["if_rot"] == pytest.approx(0.4) + assert robot_cfg.init_state.joint_pos["rf_rot"] == pytest.approx(-0.4) + assert robot_cfg.init_state.joint_pos["th_cmc"] == pytest.approx(1.45) + assert object_cfg.init_state.pos == pytest.approx((-0.092, 0.055, 0.27)) + + @pytest.mark.slow def test_dex_manip_training_smoke(device: str) -> None: _import_tasks() diff --git a/tests/test_offscreen_renderer.py b/tests/test_offscreen_renderer.py new file mode 100644 index 000000000..c9a224fbc --- /dev/null +++ b/tests/test_offscreen_renderer.py @@ -0,0 +1,33 @@ +from __future__ import annotations + +from types import SimpleNamespace + +import numpy as np +import torch + +from mjlab.viewer.offscreen_renderer import _copy_per_env_render_fields + + +def test_copy_per_env_render_fields_updates_geom_dataid() -> None: + mj_model = SimpleNamespace(geom_dataid=np.array([10, 11, 12], dtype=np.int32)) + batched_model = SimpleNamespace( + geom_dataid=torch.tensor( + [ + [1, 2, 3], + [4, 5, 6], + ], + dtype=torch.int32, + ) + ) + + _copy_per_env_render_fields(mj_model, batched_model, env_idx=1) + + assert np.array_equal(mj_model.geom_dataid, np.array([4, 5, 6], dtype=np.int32)) + + +def test_copy_per_env_render_fields_noop_without_batched_field() -> None: + mj_model = SimpleNamespace(geom_dataid=np.array([7, 8], dtype=np.int32)) + + _copy_per_env_render_fields(mj_model, batched_model=None, env_idx=0) + + assert np.array_equal(mj_model.geom_dataid, np.array([7, 8], dtype=np.int32)) From a1dd6d889974b64a6a259230bd6b4b06631c263c Mon Sep 17 00:00:00 2001 From: Andrew Rosemberg Date: Sat, 14 Mar 2026 01:54:39 -0400 Subject: [PATCH 3/3] clean assets --- src/mjlab/asset_zoo/robots/leap_hand/xmls/LICENSE | 7 ------- 1 file changed, 7 deletions(-) delete mode 100644 src/mjlab/asset_zoo/robots/leap_hand/xmls/LICENSE diff --git a/src/mjlab/asset_zoo/robots/leap_hand/xmls/LICENSE b/src/mjlab/asset_zoo/robots/leap_hand/xmls/LICENSE deleted file mode 100644 index 8017908f3..000000000 --- a/src/mjlab/asset_zoo/robots/leap_hand/xmls/LICENSE +++ /dev/null @@ -1,7 +0,0 @@ -Copyright 2023 Ananye Agarwal - -Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: - -The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. - -THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.