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gpudad/
act_so101_chunk40_250k_v1

Name: act_so101_chunk40_250k_v1
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ACT SO101 PickCube (chunk40, 250k) — v1

This repository contains an Action Chunking Transformer (ACT) policy trained for the SO-101 MuJoCo simulation pick-and-place task: pick up the red cube and place it in the blue bin.

Task

Environment: SO101PickCube-v0 (lerobot.envs.so101_sim)
Randomization: randomized cube position + randomized drop-zone/bin position

Inputs / Outputs

Observations
- observation.images.front: RGB image (3×128×128)
- observation.images.wrist: RGB image (3×128×128)
- observation.state: 10D state vector (agent_pos from the env)
Action: 4D action [dx, dy, dz, gripper]
- dx, dy, dz are end-effector delta commands in ([-1, 1])
- gripper is in ([0, 2]) where 0=open, 1=stay, 2=close

Normalization

This policy was trained with:

Images: ImageNet mean/std
State & Action: MEAN_STD using the included stats files:
- policy_preprocessor_step_3_normalizer_processor.safetensors
- policy_postprocessor_step_0_unnormalizer_processor.safetensors

Model

Policy: ACT (Action Chunking Transformer)
Vision backbone: ResNet-18 (ResNet18_Weights.IMAGENET1K_V1)
Chunking: chunk_size=40, n_action_steps=40
Transformer: dim_model=512, n_heads=8, n_encoder_layers=4, n_decoder_layers=1
VAE: enabled (latent_dim=32, kl_weight=10.0)

Training

Key settings (see train_config.json for full config):

Steps: 250,000
Batch size: 8
Optimizer: AdamW (lr=1e-5, weight_decay=1e-4)
Dataset: recorded locally as local/so101_safe_worker1 during training (not published on the Hub)

Usage

Load

python

from lerobot.policies.act.modeling_act import ACTPolicy

policy = ACTPolicy.from_pretrained("gpudad/act_so101_chunk40_250k_v1")
policy.eval()

Evaluate in the SO101 simulator

If you’re using the evaluation scripts in this repo (like eval_so101.py), download the snapshot locally first so the script can read config.json and the normalization stats files:

python

from huggingface_hub import snapshot_download

local_dir = snapshot_download("gpudad/act_so101_chunk40_250k_v1")
print(local_dir)

Then run:

python eval_so101.py --model <local_dir> --episodes 10 --max-steps 250 --no-viewer

Note: SO101PickCube-v0 reports success as info["succeed"].

Limitations

This policy is intended for the specific observation layout + environment settings used by SO101PickCube-v0.
Performance can vary with MuJoCo version, rendering settings, and random seeds.

Citation

If you use ACT, please cite:

bibtex

@article{zhao2023learning,
  title   = {Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware},
  author  = {Zhao, Tony Z. and others},
  journal = {arXiv preprint arXiv:2304.13705},
  year    = {2023}
}