Genesis-Intelligence/functional-grasp-demos

Selected Grasp Demos

Contents

Each case folder contains:

• —grasp_data.npz — Top-1 ranked grasp (pregraspdofs, grasptargetdofs, reward, z_lift)
• —image_grasp.png — AI-generated grasp image (input to perception pipeline)
• —debug_retarget.png — WujiHand retarget visualization (if available)

Cases

Replay

Setup

pip install genesis-world numpy scipy imageio Pillow trimesh

Run replay (generates video)

cd selected_demos
python replay_dynamics.py --case bowl_rot000__grasp_03 --save_video
python replay_dynamics.py --case paper_coffee_cup_rot090__grasp_06 --save_video

Grasp data format

import numpy as np
data = np.load("bowl_rot000__grasp_03/grasp_data.npz")
pre_grasp = data["pre_grasp_dofs"]      # (26,) = [xyz(3), euler(3), fingers(20)]
grasp_target = data["grasp_target_dofs"] # (26,) = pre_grasp + closing delta
reward = float(data["reward"])           # composite reward score
z_lift = float(data["z_lift"])           # how much the object lifted (meters)

Replay logic

1. 1.Place hand at pre_grasp_dofs (setpos, setquat, setdofsposition)
2. 2.PD-control fingers to grasp_target_dofs for 100 steps (closing action)
3. 3.PD-control wrist z += 0.2m for 100 steps (lifting)

Physics settings

• —dt=0.01, substeps=5, gravity=(0,0,-9.8)
• —friction=5.0, noslip_iterations=10
• —PD gains: kp=[800]6+[500]20, kv=[100]6+[50]20
• —Object mass: 0.05 per link