Warning

The Interbotix ALOHA stack that has recently been updated to v2.0. A full announcement is coming soon.

Aloha Solo Training & Evaluation

Note

ACT Solo is supported exclusively with Aloha 2.0. There is no support for Aloha Solo on Aloha 1.0. Ensure that you are using compatible software versions to avoid any issues during training or evaluation.

Install Dependencies

Install the necessary dependencies inside your containerized environment:

$ 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

Clone ACT Solo Repository

Clone ACT Solo if using Aloha Solo

$ cd ~
$ git clone https://github.com/Interbotix/act_solo.git act_training_evaluation

Build and Install ACT Solo Models

 ├── act
 │   ├── assets
 │   ├── constants.py
 │   ├── detr
 │   ├── ee_sim_env.py
 │   ├── imitate_episodes.py
 │   ├── __init__.py
 │   ├── policy.py
 │   ├── record_sim_episodes.py
 │   ├── scripted_policy.py
 │   ├── sim_env.py
 │   ├── utils.py
 │   └── visualize_episodes.py
 ├── COLCON_IGNORE
 ├── conda_env.yaml
 ├── LICENSE
 └── README.md

Navigate to the detr directory inside the repository and install the detr module which contains the model definitions using the below command:

$ cd /path/to/act_solo/ && pip install -e .

Training

To start the training, follow the steps below:

1. Sanity Check:

   Ensure you have all the hdf5 episodes located in the correct folder after following the data collection steps Task Creation.

2. Source ROS Environment:

   $ source /opt/ros/humble/setup.bash
   $ source interbotix_ws/install/setup.bash
   

3. Activate Virtual Environment:

   $ source act/bin/activate
   

4. Start Training

   $ cd /path/to/act/repository/
   $ python3 imitate_episodes.py \
     --task_name aloha_solo_dummy \
     --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
   

Note

• task_name argument should match one of the task names in the tasks_config.yaml, as configured in the Task Creation section.
• ckpt_dir: The relative location where the checkpoints and best policy will be stored.
• policy_class: Determines the choice of policy ‘ACT’/’CNNMLP’.
• kl_weight: Controls the balance between exploration and exploitation.
• chunk_size: Determines the length of the action sequence. K=1 is no action chunking and K=episode length is full open loop control.
• batch_size: Low batch size leads to better generalization and high batch size results in slower convergence but faster training time.
• num_epochs: Too many epochs lead to overfitting; too few epochs may not allow the model to learn.
• lr: Higher learning rate can lead to faster convergence but may overshoot the optima, while lower learning rate might lead to slower but stable optimization.

Tip

We recommend the following parameters:

Parameter	Value
Policy Class	ACT
KL Weight	10
Chunk Size	100
Batch Size	2
Num of Epochs	3000
Learning Rate	1e-5

Evaluation

To evaluate a trained model, follow the steps below:

1. Bringup the ALOHA

2. Configure the environment

   $ source /opt/ros/humble/setup.bash  # Configure ROS system install environment
   $ source interbotix_ws/install/setup.bash  # Configure ROS workspace environment
   $ source /<path_to_aloha_venv>/bin/activate  # Configure ALOHA Python environment
   $ cd ~/<act_repository>/act_solo/
   

3. Run the evaluation script

    python3 imitate_episodes.py \
     --task_name aloha_solo_dummy \
     --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 \
     --eval \
     --temporal_agg
   

Note

• The task_name argument should match one of the task names in the tasks_config.yaml, as configured in the Task Creation section.
• The ckpt_dir argument should match the correct relative directory location of the trained policy.
• The eval flag will set the script into evaluation mode.
• The temporal_agg is not required, but helps to smoothen the trajectory of the robots.