soarm_amazing_hand_act

SO-ARM101 + AmazingHand — ACT Grasping Policy

An ACT (Action Chunking Transformer) imitation-learning policy trained on an SO-ARM101 follower arm equipped with an AmazingHand dexterous hand, performing a cube pick-up task.

Hardware

Implementation notes: The hand is driven through rustypot (Scs0009PyController) rather than lerobot's Feetech bus — SCS0009 (protocol 1) and STS3215 (protocol 0) cannot share a bus. The leader gripper position is mapped linearly onto the hand's open/close pose.

Training Data

• Dataset: Juxi-Technology/soarm_amazing_hand_pick
• 20 teleoperated demonstrations, 18,538 frames, 30 fps
• Task: Pick up the cube with the dexterous hand
• The cube was recorded at 4 different table positions, 5 episodes each, to cover positional variation

Training Details

Joint order (6-dim): shoulder_pan.pos, shoulder_lift.pos, elbow_flex.pos, wrist_flex.pos, wrist_roll.pos, gripper.pos

gripper.pos is a virtual dimension — the leader gripper opening, mapped to the hand's grasp pose.

Usage

Requires an environment matching this project (a customized lerobot that includes the so_amazing_hand robot definition):

pip install -e ".[amazinghand,training]"

lerobot-rollout \
  --strategy.type=base \
  --policy.path=Juxi-Technology/soarm_amazing_hand_act \
  --device=cuda \
  --robot.type=so101_amazing_hand \
  --robot.port=<follower-port> \
  --robot.hand_port=<hand-port> \
  --robot.id=amazing_hand_follower \
  --robot.cameras='{
    top:   {type: opencv, index_or_path: <top-camera-index>,   width: 640, height: 480, fps: 30},
    wrist: {type: opencv, index_or_path: <wrist-camera-index>, width: 640, height: 480, fps: 30}
  }' \
  --task="Pick up the cube with the dexterous hand" \
  --duration=60

Preconditions:

1. --task must match the training single_task string exactly
2. Camera names and index order must match training
3. The robot must already be calibrated (hand_angles.json, per-joint arm calibration files)
4. The hand must be on its own serial port with an independent power supply

Limitations

• Limited positional generalization: performs well near the 4 recorded cube positions; success rate drops noticeably elsewhere
• Single-shot policy: does not loop autonomously after completing a grasp — the object and the arm must be reset manually
• Environment sensitive: collected under a single scene and lighting condition; changes to camera pose, lighting, or background degrade performance
• GPU deployment required: CPU inference runs at ~4 Hz (against a 30 Hz target), making motion visibly slow — use an NVIDIA GPU (--device=cuda)

Related

• LeRobot — training and deployment framework
• SO-ARM101 — open-source 6-DoF robotic arm
• AmazingHand — open-source dexterous hand