Change typing Optional to | to consistent with python 3.10

embodichain/agents/rl/algo/base.py

@@ -16,7 +16,7 @@
 
 from __future__ import annotations
 
-from typing import Dict, Any, Optional, Callable
+from typing import Dict, Any, Callable
 import torch
 
 
@@ -42,7 +42,7 @@ def collect_rollout(
         policy,
         obs: torch.Tensor,
         num_steps: int,
-        on_step_callback: Optional[Callable] = None,
+        on_step_callback: Callable | None = None,
     ) -> Dict[str, Any]:
         """Collect trajectories and return logging info (e.g., reward components)."""
         raise NotImplementedError

embodichain/agents/rl/algo/ppo.py

@@ -15,7 +15,7 @@
 # ----------------------------------------------------------------------------
 
 import torch
-from typing import Dict, Any, Tuple, Callable, Optional
+from typing import Dict, Any, Tuple, Callable
 
 from embodichain.agents.rl.utils import AlgorithmCfg
 from embodichain.agents.rl.buffer import RolloutBuffer
@@ -41,7 +41,7 @@ def __init__(self, cfg: PPOCfg, policy):
         self.policy = policy
         self.device = torch.device(cfg.device)
         self.optimizer = torch.optim.Adam(policy.parameters(), lr=cfg.learning_rate)
-        self.buffer: Optional[RolloutBuffer] = None
+        self.buffer: RolloutBuffer | None = None
         # no per-rollout aggregation for dense logging
 
     def _compute_gae(
@@ -76,7 +76,7 @@ def collect_rollout(
         policy,
         obs: torch.Tensor,
         num_steps: int,
-        on_step_callback: Optional[Callable] = None,
+        on_step_callback: Callable | None = None,
     ) -> Dict[str, Any]:
         """Collect a rollout. Algorithm controls the data collection process."""
         if self.buffer is None:

embodichain/agents/rl/models/mlp.py

@@ -17,7 +17,7 @@
 from __future__ import annotations
 
 from functools import reduce
-from typing import Iterable, List, Optional, Sequence, Tuple, Union
+from typing import Iterable, List, Sequence, Tuple, Union
 
 import torch
 import torch.nn as nn

embodichain/agents/rl/utils/trainer.py

@@ -16,7 +16,7 @@
 
 from __future__ import annotations
 
-from typing import Dict, Any, Tuple, Callable, Optional
+from typing import Dict, Any, Tuple, Callable
 import time
 import numpy as np
 import torch

embodichain/lab/gym/envs/action_bank/configurable_action.py

@@ -21,7 +21,7 @@
 import matplotlib.pyplot as plt
 
 from copy import deepcopy
-from typing import Dict, Tuple, Union, List, Callable, Any, Optional
+from typing import Dict, Tuple, Union, List, Callable, Any
 from tqdm import tqdm
 from functools import partial
 

embodichain/lab/gym/envs/action_bank/utils.py

@@ -16,7 +16,7 @@
 import numpy as np
 
 from copy import deepcopy
-from typing import List, Union, Optional
+from typing import List
 
 from embodichain.utils import logger
 from embodichain.lab.gym.utils.misc import validation_with_process_from_name
@@ -33,7 +33,7 @@ def generate_affordance_from_src(
     env,
     src_key: str,
     dst_key: str,
-    valid_funcs_name_kwargs_proc: Optional[List] = None,
+    valid_funcs_name_kwargs_proc: list | None = None,
     to_array: bool = True,
 ) -> bool:
     """Generate a new affordance entry in env.affordance_datas by applying a validation and processing
@@ -43,7 +43,7 @@ def generate_affordance_from_src(
         env: The environment object containing affordance data.
         src_key (str): The key of the source affordance in env.affordance_datas.
         dst_key (str): The key to store the generated affordance in env.affordance_datas.
-        valid_funcs_name_kwargs_proc (Optional[List]): A list of validation or processing functions (with kwargs)
+        valid_funcs_name_kwargs_proc (list | None): A list of validation or processing functions (with kwargs)
             to apply to the source affordance. Defaults to an empty list.
         to_array (bool): Whether to convert the result to a numpy array before storing. Defaults to True.
 

embodichain/lab/gym/envs/base_env.py

@@ -17,7 +17,7 @@
 import torch
 import gymnasium as gym
 
-from typing import Dict, List, Union, Tuple, Any, Optional, Sequence
+from typing import Dict, List, Union, Tuple, Any, Sequence
 from functools import cached_property
 
 from embodichain.lab.sim.types import EnvObs, EnvAction
@@ -41,7 +41,7 @@ class EnvCfg:
     sim_cfg: SimulationManagerCfg = SimulationManagerCfg()
     """Simulation configuration for the environment."""
 
-    seed: Optional[int] = None
+    seed: int | None = None
     """The seed for the random number generator. Defaults to -1, in which case the seed is not set.
 
     Note:
@@ -272,7 +272,7 @@ def _update_sim_state(self, **kwargs):
         # TODO: Add randomization event here.
         pass
 
-    def _initialize_episode(self, env_ids: Optional[Sequence[int]] = None, **kwargs):
+    def _initialize_episode(self, env_ids: Sequence[int] | None = None, **kwargs):
         """Initialize the simulation assets before each episode. Randomization can be performed at this stage.
 
         Args:
@@ -427,7 +427,7 @@ def _step_action(self, action: EnvAction) -> EnvAction:
         pass
 
     def reset(
-        self, seed: Optional[int] = None, options: Optional[Dict] = None
+        self, seed: int | None = None, options: dict | None = None
     ) -> Tuple[EnvObs, Dict]:
         """Reset the SimulationManager environment and return the observation and info.
 

embodichain/lab/gym/envs/embodied_env.py

@@ -20,7 +20,7 @@
 import gymnasium as gym
 
 from dataclasses import MISSING
-from typing import Dict, Union, Optional, Sequence, Tuple, Any, List
+from typing import Dict, Union, Sequence, Tuple, Any, List
 
 from embodichain.lab.sim.cfg import (
     RobotCfg,
@@ -253,7 +253,7 @@ def get_affordance(self, key: str, default: Any = None):
         return self.affordance_datas.get(key, default)
 
     def reset(
-        self, seed: Optional[int] = None, options: Optional[Dict] = None
+        self, seed: int | None = None, options: dict | None = None
     ) -> Tuple[EnvObs, Dict]:
         obs, info = super().reset(seed=seed, options=options)
 
@@ -297,7 +297,7 @@ def _update_sim_state(self, **kwargs) -> None:
                 self.event_manager.apply(mode="interval")
 
     def _initialize_episode(
-        self, env_ids: Optional[Sequence[int]] = None, **kwargs
+        self, env_ids: Sequence[int] | None = None, **kwargs
     ) -> None:
         # apply events such as randomization for environments that need a reset
         if self.cfg.events:
@@ -439,28 +439,28 @@ def preview_sensor_data(
             plt.imshow(view)
             plt.savefig(f"sensor_data_{data_type}.png")
 
-    def create_demo_action_list(self, *args, **kwargs) -> Optional[Sequence[EnvAction]]:
+    def create_demo_action_list(self, *args, **kwargs) -> Sequence[EnvAction] | None:
         """Create a demonstration action list for the environment.
 
         This function should be implemented in subclasses to generate a sequence of actions
         that demonstrate a specific task or behavior within the environment.
 
         Returns:
-            Optional[Sequence[EnvAction]]: A list of actions if a demonstration is available, otherwise None.
+            Sequence[EnvAction] | None: A list of actions if a demonstration is available, otherwise None.
         """
         raise NotImplementedError(
             "The method 'create_demo_action_list' must be implemented in subclasses."
         )
 
-    def to_dataset(self, id: str, save_path: str = None) -> Optional[str]:
+    def to_dataset(self, id: str, save_path: str = None) -> str | None:
         """Convert the recorded episode data to a dataset format.
 
         Args:
             id (str): Unique identifier for the dataset.
             save_path (str, optional): Path to save the dataset. If None, use config or default.
 
         Returns:
-            Optional[str]: The path to the saved dataset, or None if failed.
+            str | None: The path to the saved dataset, or None if failed.
         """
         raise NotImplementedError(
             "The method 'to_dataset' will be implemented in the near future."

embodichain/lab/gym/envs/managers/observations.py

@@ -19,7 +19,7 @@
 import torch
 import os
 import random
-from typing import TYPE_CHECKING, Literal, Union, Optional, List, Dict, Sequence
+from typing import TYPE_CHECKING, Literal, Union, List, Dict, Sequence
 
 from embodichain.lab.sim.objects import RigidObject, Articulation, Robot
 from embodichain.lab.sim.sensors import Camera, StereoCamera
@@ -388,7 +388,7 @@ def _project_3d_to_2d(
         return points_2d
 
     def _get_gripper_ratio(
-        self, control_part: str, gripper_qpos: Optional[torch.Tensor] = None
+        self, control_part: str, gripper_qpos: torch.Tensor | None = None
     ):
         robot: Robot = self._env.robot
         gripper_max_limit = robot.body_data.qpos_limits[
@@ -402,11 +402,11 @@ def _get_gripper_ratio(
 
     def _get_robot_exteroception(
         self,
-        control_part: Optional[str] = None,
+        control_part: str | None = None,
         x_interval: float = 0.02,
         y_interval: float = 0.02,
         kpnts_number: int = 12,
-        offset: Optional[Union[List, torch.Tensor]] = None,
+        offset: list | torch.Tensor | None = None,
         follow_eef: bool = False,
     ) -> torch.Tensor:
         """Get the robot exteroception poses.
@@ -468,7 +468,7 @@ def _get_object_exteroception(
         y_interval: float = 0.02,
         kpnts_number: int = 12,
         is_arena_coord: bool = False,
-        follow_eef: Optional[str] = None,
+        follow_eef: str | None = None,
     ) -> torch.Tensor:
         """Get the rigid object exteroception poses.
 

embodichain/lab/gym/envs/managers/randomization/rendering.py

@@ -19,7 +19,7 @@
 import torch
 import os
 import random
-from typing import TYPE_CHECKING, Literal, Union, Optional, Dict
+from typing import TYPE_CHECKING, Literal, Union, Dict
 
 from embodichain.lab.sim.objects import Light, RigidObject, Articulation
 from embodichain.lab.sim.sensors import Camera, StereoCamera
@@ -55,11 +55,11 @@ def randomize_camera_extrinsics(
     env: EmbodiedEnv,
     env_ids: Union[torch.Tensor, None],
     entity_cfg: SceneEntityCfg,
-    pos_range: Optional[tuple[list[float], list[float]]] = None,
-    euler_range: Optional[tuple[list[float], list[float]]] = None,
-    eye_range: Optional[tuple[list[float], list[float]]] = None,
-    target_range: Optional[tuple[list[float], list[float]]] = None,
-    up_range: Optional[tuple[list[float], list[float]]] = None,
+    pos_range: tuple[list[float], list[float]] | None = None,
+    euler_range: tuple[list[float], list[float]] | None = None,
+    eye_range: tuple[list[float], list[float]] | None = None,
+    target_range: tuple[list[float], list[float]] | None = None,
+    up_range: tuple[list[float], list[float]] | None = None,
 ) -> None:
     """
     Randomize camera extrinsic properties (position and orientation).
@@ -177,9 +177,9 @@ def randomize_light(
     env: EmbodiedEnv,
     env_ids: Union[torch.Tensor, None],
     entity_cfg: SceneEntityCfg,
-    position_range: Optional[tuple[list[float], list[float]]] = None,
-    color_range: Optional[tuple[list[float], list[float]]] = None,
-    intensity_range: Optional[tuple[float, float]] = None,
+    position_range: tuple[list[float], list[float]] | None = None,
+    color_range: tuple[list[float], list[float]] | None = None,
+    intensity_range: tuple[float, float] | None = None,
 ) -> None:
     """Randomize light properties by adding, scaling, or setting random values.
 
@@ -205,9 +205,9 @@ def randomize_light(
         env (EmbodiedEnv): The environment instance.
         env_ids (Union[torch.Tensor, None]): The environment IDs to apply the randomization.
         entity_cfg (SceneEntityCfg): The configuration of the scene entity to randomize.
-        position_range (Optional[tuple[list[float], list[float]]]): The range for the position randomization.
-        color_range (Optional[tuple[list[float], list[float]]]): The range for the color randomization.
-        intensity_range (Optional[tuple[float, float]]): The range for the intensity randomization.
+        position_range (tuple[list[float], list[float]] | None): The range for the position randomization.
+        color_range (tuple[list[float], list[float]] | None): The range for the color randomization.
+        intensity_range (tuple[float, float] | None): The range for the intensity randomization.
     """
 
     light: Light = env.sim.get_light(entity_cfg.uid)
@@ -259,10 +259,10 @@ def randomize_camera_intrinsics(
     env: EmbodiedEnv,
     env_ids: Union[torch.Tensor, None],
     entity_cfg: SceneEntityCfg,
-    focal_x_range: Optional[tuple[float, float]] = None,
-    focal_y_range: Optional[tuple[float, float]] = None,
-    cx_range: Optional[tuple[float, float]] = None,
-    cy_range: Optional[tuple[float, float]] = None,
+    focal_x_range: tuple[float, float] | None = None,
+    focal_y_range: tuple[float, float] | None = None,
+    cx_range: tuple[float, float] | None = None,
+    cy_range: tuple[float, float] | None = None,
 ) -> None:
     """Randomize camera intrinsic properties by adding, scaling, or setting random values.
 
@@ -289,10 +289,10 @@ def randomize_camera_intrinsics(
         env (EmbodiedEnv): The environment instance.
         env_ids (Union[torch.Tensor, None]): The environment IDs to apply the randomization.
         entity_cfg (SceneEntityCfg): The configuration of the scene entity to randomize.
-        focal_x_range (Optional[tuple[float, float]]): The range for the focal length x randomization.
-        focal_y_range (Optional[tuple[float, float]]): The range for the focal length y randomization.
-        cx_range (Optional[tuple[float, float]]): The range for the principal point x randomization.
-        cy_range (Optional[tuple[float, float]]): The range for the principal point y randomization.
+        focal_x_range (tuple[float, float] | None): The range for the focal length x randomization.
+        focal_y_range (tuple[float, float] | None): The range for the focal length y randomization.
+        cx_range (tuple[float, float] | None): The range for the principal point x randomization.
+        cy_range (tuple[float, float] | None): The range for the principal point y randomization.
     """
 
     camera: Union[Camera, StereoCamera] = env.sim.get_sensor(entity_cfg.uid)
@@ -500,11 +500,11 @@ def __call__(
         env_ids: Union[torch.Tensor, None],
         entity_cfg: SceneEntityCfg,
         random_texture_prob: float = 0.5,
-        texture_path: Optional[str] = None,
-        base_color_range: Optional[tuple[list[float], list[float]]] = None,
-        metallic_range: Optional[tuple[float, float]] = None,
-        roughness_range: Optional[tuple[float, float]] = None,
-        ior_range: Optional[tuple[float, float]] = None,
+        texture_path: str | None = None,
+        base_color_range: tuple[list[float], list[float]] | None = None,
+        metallic_range: tuple[float, float] | None = None,
+        roughness_range: tuple[float, float] | None = None,
+        ior_range: tuple[float, float] | None = None,
     ):
         from embodichain.lab.sim.utility import is_rt_enabled