• imitate_episodes.py Train and Evaluate ACT
• policy.py An adaptor for ACT policy
• detr Model definitions of ACT, modified from DETR
• sim_env.py Mujoco + DM_Control environments with joint space control
• ee_sim_env.py Mujoco + DM_Control environments with EE space control
• scripted_policy.py Scripted policies for sim environments
• constants.py Constants shared across files
• utils.py Utils such as data loading and helper functions
• visualize_episodes.py Save videos from a .hdf5 dataset

conda create -n aloha python=3.8.10
conda activate aloha
pip install torchvision
pip install torch
pip install pyquaternion
pip install pyyaml
pip install rospkg
pip install pexpect
pip install mujoco==2.3.7
pip install dm_control==1.0.14
pip install opencv-python
pip install matplotlib
pip install einops
pip install packaging
pip install h5py
pip install ipython
cd act/detr && pip install -e .

To set up a new terminal, run:

conda activate aloha
cd <path to act repo>

We use sim_transfer_cube_scripted task in the examples below. Another option is sim_insertion_scripted. To generated 50 episodes of scripted data, run:

python3 record_sim_episodes.py \
--task_name sim_transfer_cube_scripted \
--dataset_dir <data save dir> \
--num_episodes 50

To can add the flag --onscreen_render to see real-time rendering. To visualize the episode after it is collected, run

python3 visualize_episodes.py --dataset_dir <data save dir> --episode_idx 0

To train ACT:

# Transfer Cube task
python3 imitate_episodes.py \
--task_name sim_transfer_cube_scripted \
--ckpt_dir <ckpt dir> \
--policy_class ACT --kl_weight 10 --chunk_size 100 --hidden_dim 512 --batch_size 8 --dim_feedforward 3200 \
--num_epochs 2000  --lr 1e-5 \
--seed 0
--pose_mode

To evaluate the policy, run the same command but add --eval. This loads the best validation checkpoint. The success rate should be around 90% for transfer cube, and around 50% for insertion. To enable temporal ensembling, add flag --temporal_agg. Videos will be saved to <ckpt_dir> for each rollout. You can also add --onscreen_render to see real-time rendering during evaluation. Here when doing peg insertion, the --pose_mode could be set to similar and edge, random is the default mode

To see trajectory:

python3 trajectory_analysis_with_plots.py

To visualize the Z:

python3 z_analysis.py