Adversarial Skill Chaining for Long-Horizon Robot Manipulation via Terminal State Regularization

[Project website] [Paper]

This project is a PyTorch implementation of Adversarial Skill Chaining for Long-Horizon Robot Manipulation via Terminal State Regularization, published in CoRL 2021.

Note that Unity rendering for IKEA Furniture Assembly Environment is temporally not available due to the deprecated Unity-MuJoCo plugin in the new version of MuJoCo (2.1). It is still working with MuJoCo 2.0.

Files and Directories

• run.py: launches an appropriate trainer based on algorithm
• policy_sequencing_trainer.py: trainer for policy sequencing
• policy_sequencing_agent.py: model and training code for policy sequencing
• policy_sequencing_rollout.py: rollout with policy sequencing agent
• policy_sequencing_config.py: hyperparameters
• method/: implementation of IL and RL algorithms
• furniture/: IKEA furniture environment
• demos/: default demonstration directory
• log/: default training log directory
• result/: evaluation result directory

Prerequisites

• Ubuntu 18.04 or above
• Python 3.6
• Mujoco 2.1

Installation

0. Clone this repository and submodules.

$ git clone --recursive [email protected]:clvrai/skill-chaining.git

1. Install mujoco 2.1 and add the following environment variables into ~/.bashrc or ~/.zshrc Note that the code is compatible with MuJoCo 2.0, which supports Unity rendering.

# download mujoco 2.1
$ mkdir ~/.mujoco
$ wget https://mujoco.org/download/mujoco210-linux-x86_64.tar.gz -O mujoco_linux.tar.gz
$ tar -xvzf mujoco_linux.tar.gz -C ~/.mujoco/
$ rm mujoco_linux.tar.gz

# add mujoco to LD_LIBRARY_PATH
$ export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$HOME/.mujoco/mujoco210/bin

# for GPU rendering
$ export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/lib/nvidia

# only for a headless server
$ export LD_PRELOAD=/usr/lib/x86_64-linux-gnu/libGLEW.so

2. Install python dependencies

$ sudo apt-get install cmake libopenmpi-dev libgl1-mesa-dev libgl1-mesa-glx libosmesa6-dev patchelf libglew-dev

# software rendering
$ sudo apt-get install libgl1-mesa-glx libosmesa6 patchelf

# window rendering
$ sudo apt-get install libglfw3 libglew2.0

3. Install furniture submodule

$ cd furniture
$ pip install -e .
$ cd ../method
$ pip install -e .
$ pip install torch torchvision

Usage

For chair_ingolf_0650, simply change table_lack_0825 to chair_ingolf_0650 in the commands. For training with gpu, specify the desired gpu number (e.g. --gpu 0). To change the random seed, append, e.g., --seed 0 to the command.

To enable wandb logging, add the following arguments with your wandb entity and project names: --wandb True --wandb_entity [WANDB ENTITY] --wandb_project [WANDB_PROJECT].

1. Generate demos

# Sub-task demo generation
python -m furniture.env.furniture_sawyer_gen --furniture_name table_lack_0825 --demo_dir demos/table_lack/ --reset_robot_after_attach True --max_episode_steps 200 --num_connects 1 --n_demos 200 --start_count 0 --phase_ob True
python -m furniture.env.furniture_sawyer_gen --furniture_name table_lack_0825 --demo_dir demos/table_lack/ --reset_robot_after_attach True --max_episode_steps 200 --num_connects 1 --n_demos 200 --preassembled 0 --start_count 1000 --phase_ob True
python -m furniture.env.furniture_sawyer_gen --furniture_name table_lack_0825 --demo_dir demos/table_lack/ --reset_robot_after_attach True --max_episode_steps 200 --num_connects 1 --n_demos 200 --preassembled 0,1 --start_count 2000 --phase_ob True
python -m furniture.env.furniture_sawyer_gen --furniture_name table_lack_0825 --demo_dir demos/table_lack/ --reset_robot_after_attach True --max_episode_steps 200 --num_connects 1 --n_demos 200 --preassembled 0,1,2 --start_count 3000 --phase_ob True

# Full-task demo generation
python -m furniture.env.furniture_sawyer_gen --furniture_name table_lack_0825 --demo_dir demos/table_lack_full/ --reset_robot_after_attach True --max_episode_steps 800 --num_connects 4 --n_demos 200 --start_count 0 --phase_ob True

2. Train sub-task policies

mpirun -np 16 python -m run --algo gail --furniture_name table_lack_0825 --demo_path demos/table_lack/Sawyer_table_lack_0825_0 --num_connects 1 --run_prefix p0
mpirun -np 16 python -m run --algo gail --furniture_name table_lack_0825 --demo_path demos/table_lack/Sawyer_table_lack_0825_1 --num_connects 1 --preassembled 0 --run_prefix p1 --load_init_states log/table_lack_0825.gail.p0.123/success_00024576000.pkl
mpirun -np 16 python -m run --algo gail --furniture_name table_lack_0825 --demo_path demos/table_lack/Sawyer_table_lack_0825_2 --num_connects 1 --preassembled 0,1 --run_prefix p2 --load_init_states log/table_lack_0825.gail.p1.123/success_00030310400.pkl
mpirun -np 16 python -m run --algo gail --furniture_name table_lack_0825 --demo_path demos/table_lack/Sawyer_table_lack_0825_3 --num_connects 1 --preassembled 0,1,2 --run_prefix p3 --load_init_states log/table_lack_0825.gail.p2.123/success_00027852800.pkl

3. Collect successful terminal states from sub-task policies Find the best performing checkpoint from WandB, and replace checkpoint path with the best performing checkpoint (e.g. --init_ckpt_path log/table_lack_0825.gail.p0.123/ckpt_00021299200.pt).

python -m run --algo gail --furniture_name table_lack_0825 --demo_path demos/table_lack/Sawyer_table_lack_0825_0 --num_connects 1 --run_prefix p0 --is_train False --num_eval 200 --record_video False --init_ckpt_path log/table_lack_0825.gail.p0.123/ckpt_00000000000.pt
python -m run --algo gail --furniture_name table_lack_0825 --demo_path demos/table_lack/Sawyer_table_lack_0825_1 --num_connects 1 --preassembled 0 --run_prefix p1 --is_train False --num_eval 200 --record_video False --init_ckpt_path log/table_lack_0825.gail.p1.123/ckpt_00000000000.pt
python -m run --algo gail --furniture_name table_lack_0825 --demo_path demos/table_lack/Sawyer_table_lack_0825_2 --num_connects 1 --preassembled 0,1 --run_prefix p2 --is_train False --num_eval 200 --record_video False --init_ckpt_path log/table_lack_0825.gail.p2.123/ckpt_00000000000.pt
python -m run --algo gail --furniture_name table_lack_0825 --demo_path demos/table_lack/Sawyer_table_lack_0825_3 --num_connects 1 --preassembled 0,1,2 --run_prefix p3 --is_train False --num_eval 200 --record_video False --init_ckpt_path log/table_lack_0825.gail.p3.123/ckpt_00000000000.pt

4. Train skill chaining Use the best performing checkpoints (--ps_ckpt) and their successful terminal states (--ps_laod_init_states).

# Ours
mpirun -np 16 python -m run --algo ps --furniture_name table_lack_0825 --num_connects 4 --run_prefix ours \
--ps_ckpts log/table_lack_0825.gail.p0.123/ckpt_00021299200.pt,log/table_lack_0825.gail.p1.123/ckpt_00021299200.pt,log/table_lack_0825.gail.p2.123/ckpt_00021299200.pt,log/table_lack_0825.gail.p3.123/ckpt_00021299200.pt \
--ps_load_init_states log/table_lack_0825.gail.p0.123/success_00021299200.pkl,log/table_lack_0825.gail.p1.123/success_00021299200.pkl,log/table_lack_0825.gail.p2.123/success_00021299200.pkl,log/table_lack_0825.gail.p3.123/success_00021299200.pkl \
--ps_demo_paths demos/table_lack/Sawyer_table_lack_0825_0,demos/table_lack/Sawyer_table_lack_0825_1,demos/table_lack/Sawyer_table_lack_0825_2,demos/table_lack/Sawyer_table_lack_0825_3

# Policy Sequencing (Clegg et al. 2018)
mpirun -np 16 python -m run --algo ps --furniture_name table_lack_0825 --num_connects 4 --run_prefix ps \
--ps_ckpts log/table_lack_0825.gail.p0.123/ckpt_00021299200.pt,log/table_lack_0825.gail.p1.123/ckpt_00021299200.pt,log/table_lack_0825.gail.p2.123/ckpt_00021299200.pt,log/table_lack_0825.gail.p3.123/ckpt_00021299200.pt \
--ps_load_init_states log/table_lack_0825.gail.p0.123/success_00021299200.pkl,log/table_lack_0825.gail.p1.123/success_00021299200.pkl,log/table_lack_0825.gail.p2.123/success_00021299200.pkl,log/table_lack_0825.gail.p3.123/success_00021299200.pkl \
--ps_demo_paths demos/table_lack/Sawyer_table_lack_0825_0,demos/table_lack/Sawyer_table_lack_0825_1,demos/table_lack/Sawyer_table_lack_0825_2,demos/table_lack/Sawyer_table_lack_0825_3

5. Train baselines

# BC
python -m run --algo bc --max_global_step 1000 --furniture_name table_lack_0825 --demo_path demos/table_lack_full/Sawyer_table_lack_0825 --record_video False --run_prefix bc --gpu 0

# GAIL
mpirun -np 16 python -m run --algo gail --furniture_name table_lack_0825 --demo_path demos/table_lack_full/Sawyer_table_lack_0825 --num_connects 4 --max_episode_steps 800 --max_global_step 200000000 --run_prefix gail --gail_env_reward 0

# GAIL+PPO
mpirun -np 16 python -m run --algo gail --furniture_name table_lack_0825 --demo_path demos/table_lack_full/Sawyer_table_lack_0825 --num_connects 4 --max_episode_steps 800 --max_global_step 200000000 --run_prefix gail_ppo

# PPO
mpirun -np 16 python -m run --algo ppo --furniture_name table_lack_0825 --num_connects 4 --max_episode_steps 800 --max_global_step 200000000 --run_prefix ppo

Citation

If you find this useful, please cite

@inproceedings{lee2021adversarial,
  title={Adversarial Skill Chaining for Long-Horizon Robot Manipulation via Terminal State Regularization},
  author={Youngwoon Lee and Joseph J. Lim and Anima Anandkumar and Yuke Zhu},
  booktitle={Conference on Robot Learning},
  year={2021},
}

References

• This code is based on Youngwoon's robot-learning repo: https://github.com/youngwoon/robot-learning
• IKEA Furniture Assembly Environment: https://github.com/clvrai/furniture