ACT: Action Chunking with Transformers

New: ACT tuning tips

TL;DR: if your ACT policy is jerky or pauses in the middle of an episode, just train for longer! Success rate and smoothness can improve way after loss plateaus.

Project Website: https://tonyzhaozh.github.io/aloha/

This repo contains the implementation of ACT, together with 2 simulated environments: Transfer Cube and Bimanual Insertion. You can train and evaluate ACT in sim or real. For real, you would also need to install ALOHA.

Updates:

You can find all scripted/human demo for simulated environments here.

Repo Structure

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

Installation

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 .

Example Usages

To set up a new terminal, run:

conda activate aloha
cd <path to act repo>

Simulated experiments

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

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.

For real-world data where things can be harder to model, train for at least 5000 epochs or 3-4 times the length after the loss has plateaued. Please refer to tuning tips for more info.

changzhengwu/act