Imitation Learning algorithms and Co-training for Mobile ALOHA

Project Website: https://mobile-aloha.github.io/

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

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
pip install dm_control
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.

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.

lijingshanxi/act-plus-plus