Genesis-Intelligence/functional-grasp-demos

Selected Grasp Demos

Contents

Each case folder contains:

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

Cases

Case	Object	Scale	Reward	z_lift
paper_coffee_cup_rot090__grasp_06	Paper Coffee Cup	1.0	0.742	0.196
paper_cup_8_oz_rot000__grasp_06	Paper Cup 8 Oz	1.0	0.696	0.201
bowl_rot000__grasp_03	Bowl	0.95	0.764	0.211
simple_mug_rot090__grasp_06	Simple Mug	1.0	0.660	0.179
simple_mug_rot090__grasp_05	Simple Mug	1.0	0.674	0.192

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. Place hand at pre_grasp_dofs (set_pos, set_quat, set_dofs_position)
2. PD-control fingers to grasp_target_dofs for 100 steps (closing action)
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