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ACT

Set-up Envrionment

uv venv .venv-act --python 3.10
source .venv-act/bin/activate
GIT_LFS_SKIP_SMUDGE=1 uv sync --group psi --group serve --group viz --active --frozen
cp src/lerobot_patch/common/datasets/lerobot_dataset.py \
  .venv-act/lib/python3.10/site-packages/lerobot/common/datasets/lerobot_dataset.py

Download Psi0 Task Data

Download the task data, for example

export task=G1WholebodyXMovePick-v0
hf download USC-PSI-Lab/psi-data simple/$task.zip --local-dir=$PSI_HOME/data --repo-type=dataset
unzip "$PSI_HOME/data/simple/$task.zip" -d "$PSI_HOME/data/simple"

Create a new TrainConfig for the task in src/psi/config/train/real_act_config.py:

Skip this step if you are finetuning the same SIMPLE/real tasks provided by $Psi_0.

Train $ACT$

Launch the training script

bash baselines/act/train_act_g1_real.sh $task  # for train in real experiments
bash baselines/act/train_act_g1_simple.sh $task #  for train in simple

Eval $ACT$ 

export RUN_DIR=xxxx
export CKPT_STEP=40000
bash baselines/act/serve_act_g1_real.sh $RUN_DIR $CKPT_STEP # for train in real experiments
bash baselines/act/serve_act_g1_simple.sh $RUN_DIR $CKPT_STEP # for train in simple

Eval in SIMPLE

TODO: migrate following instructions using SIMPLE third_party

cd <project root of SIMPLE>
source .venv/bin/activate

export task=G1WholebodyXMovePick-v0

Download eval data and extract it:

hf download USC-PSI-Lab/psi-data \
	simple-eval/$task.zip \
	--local-dir=data/evals \
	--repo-type=dataset

unzip data/evals/simple-eval/$task.zip -d data/evals/simple-eval

Now start SIMPLE eval in the SIMPLE environment:

We provide three domain randomization levels: level-0, level-1, level-2 for each task

export dr=level-0

We use two different entrypoints for evaluating different tasks:

set entrypoint and agent to eval_decoupled_wbc.py and act_decoupled_wbc if the evaluating task ends with Teleop, which means the task data is collected using teleoperation:

export entry=eval_decoupled_wbc.py
export agent=act_decoupled_wbc

and set entrypoint and agent to eval.py and act_g1 if the evaluating task ends with MP, which means the task data is generated using CuRobo Motion planning:

export entry=eval.py
export entry=act_g1

python src/simple/cli/$entry \
	simple/$task \
	$agent \
	$dr \
	--host=localhost \
	--port=22085 \
	--sim-mode=mujoco_isaac \
	--no-headless \
	--data-format=lerobot \
	--data-dir=data/evals/simple-eval/$task/$dr