Original Project Websites:

• ALOHA
• Mobile ALOHA

This repo contains the implementation of ACT, together with 2 simulated environments:

• Transfer Cube
• Bimanual Insertion

You can train and evaluate ACT in simulation or on real hardware. For real hardware, you would also need to install ALOHA.

• act
  • detr Model definitions of ACT, modified from DETR
  • policy.py An adaptor for ACT policy
  • 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
  • scripts
    • imitate_episodes.py Train and Evaluate ACT
    • record_sim_episodes.py Record episodes using the simulator
    • visualize_episodes.py Save videos from a .hdf5 dataset

There are two recommended ways to install ACT: using conda or venv. Using venv is preferred due its ease of use against frameworks like ROS.

sudo apt-get install python3-venv
python3 -m venv ~/act # creates a venv "act" in the home directory, can be created anywhere
source ~/act/bin/activate
pip install dm_control==1.0.14
pip install einops
pip install h5py
pip install ipython
pip install matplotlib
pip install mujoco==2.3.7
pip install opencv-python
pip install packaging
pip install pexpect
pip install pyquaternion
pip install pyyaml
pip install rospkg
pip install torch
pip install torchvision
cd /path/to/act/detr && pip install -e .

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

conda env create --file=conda_env.yaml

To set up a new terminal, run:

source ~/aloha/bin/activate # or conda activate aloha
cd /path/to/act

We use sim_transfer_cube_scripted task in the examples below. Another option is sim_insertion_scripted. To generate 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.

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

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.