Written by Joohwan Seo, Ph.D. Student in Mechanical Engineering, UC Berkeley.
This is really nasty code. A lot of unnecessary function/variables are still in the code - I tried my best to cleanse them.
rlkit/core/batch_rl_algorithm.py
collect_dataset.py
is collecting expert dataset for the BC.GIC-RL_sac.py
is for the the reinforcement learning part.data_analyzer.py
is to check the learning curve during the RL agent training. - I did not upload the whole training files, only fraction or the success cases can be found indata/Fanuc_success/
run_policy_robot_BC.py
is to check the trained policy by the Behavior Cloning.run_policy_robot.py
is to check the trained policy by the RL.
Hyperparameters and the statements are denoted as annotations in each file as much as possible.
For GIC + GCEV
python scripts/run_policy_robot_BC.py data/Fanuc_success/Behavior_cloning/BC_policy_GIC_GCEV_300_itr_39.pt --benchmark False --vis True
For CIC + CEV
python scripts/run_policy_robot_BC.py data/Fanuc_success/Behavior_cloning/BC_policy_CIC_CEV_300_default_39.pt --benchmark True --vis True
If you want a mixed observation result, change mixed_obs
to True
For GIC + GCEV
python scripts/run_policy_robot.py data/Fanuc_success/final_GIC_minimal_separated_pos_3x128_reward2/itr_60.pkl --benchmark False --vis True
For CIC + CEV
python scripts/run_policy_robot.py data/Fanuc_success/final_CIC_minimal_separated_pos_3x128_reward2/itr_90.pkl --benchmark True --vis True
Berkeley RL Kit for the Reinforcement Learning
Directly Imported from https://github.com/rail-berkeley/rlkit
Environmental Setup using Mujoco
https://github.com/deanpham98/learn-seq
https://doi.org/10.48550/arXiv.2211.07945
and
https://github.com/Joohwan-Seo/Geometric-Impedance-Control-Public
export PYTHONPATH=/your_directory_to_the_GIC_Learning_public_folder:$PYTHONPATH
mujoco: 2.0.0
python: 3.6.13
cuda: 11.4 (Trained with GPU RTX3060 12GB)
Robotics and Automation Letters (RAL) and IROS 2024
Seo et al., Contact-rich SE(3)-Equivariant Robot Manipulation Task Learning via Geometric Impedance Control
@article{seo2023contact,
title={Contact-rich SE (3)-Equivariant Robot Manipulation Task Learning via Geometric Impedance Control},
author={Seo, Joohwan and Prakash, Nikhil PS and Zhang, Xiang and Wang, Changhao and Choi, Jongeun and Tomizuka, Masayoshi and Horowitz, Roberto},
journal={IEEE Robotics and Automation Letters},
year={2023},
publisher={IEEE}
}
https://sites.google.com/berkeley.edu/equivariant-task-learning/home