rope.yaml

# MIT License
#
# Copyright (c) 2022 NVIDIA CORPORATION & AFFILIATES, University of Washington. All rights reserved.
#
# 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.

training_model_parameters:
  collision_loss_weight: 5
  point_match_loss_weight: 1
  collision_loss_margin: 0.03
  min_lr: 1.0e-5
  max_lr: 7.0e-5 # May not work
  warmup_steps: 5000
  decay_rate: null # 0.999
  pc_bounds: [[-1.5, -1.5, -0.1], [1.5, 1.5, 1.5]]
  hard_negative_ratio: 0.2
  train_batch_size: 10
data_module_parameters:
  data_dir: DATA_PATH
  train_trajectory_key: "global_solutions" # Could also use 'global_solutions' for the global expert
  val_trajectory_key: "global_solutions" # Could also use 'global_solutions' for the global expert
  num_obstacle_points: 4096
  num_target_points: 128
  random_scale: 0.015
  ignore_pretrain_data: true
shared_parameters:
  prismatic_joint: 0.04
  num_robot_points: 2048
  action_chunk_length: 1
num_workers: 10
checkpoint_interval: 60
val_every_n_minutes: 60
gpus: 1 # If > 1, will use DDP for training.
train_batch_size: 10
val_batch_size: 10
max_epochs: 100
use_simple_model: false
stop_early: false
save_checkpoint_dir: /gscratch/rselab/afishman/mpinets_checkpoints
load_checkpoint_path: PATH/TO/PRETRAINING/CHECKPOINT
experiment_name: ROPEFineTuning
description: "Put in a description for the ROPE experiment"