Official code for DemoGrasp: Universal Dexterous Grasping from a Single Demonstration (ICLR 2026).
DemoGrasp is a framework for learning universal dexterous grasping policies via reinforcement learning (RL) augmented with a single demonstration. It achieves state-of-the-art performance across diverse robotic hand embodiments and transfers effectively to real robots, demonstrating strong generalization.
- Create a conda environment with python 3.8.19, then install packages:
conda create -n demograsp python=3.8.19
conda activate demograsp
pip install torch==2.3.0+cu121 torchvision==0.18.0+cu121 torchaudio==2.3.0 --index-url https://download.pytorch.org/whl/cu121
pip install numpy==1.23.5 sapien==3.0.0b0 chumpy==0.70 addict==2.4.0 yapf==0.43.0 h5py sorcery==0.2.2 pynvml==11.5.0 ipdb==0.13.13 matplotlib==3.5.1 networkx==3.1 tqdm==4.66.4 datasets==3.1.0 einops==0.4.1 jsonlines
-
Install IsaacGym Preview 4 and IsaacGymEnvs.
-
Download assets and checkpoints from this link.
- Unzip
robots.zip,union_ycb_unidex.zip, andtextures.zipinto theassets/folder. - Unzip
ckpt.zipinto the project root.
- Unzip
-
Your project structure should look like:
DemoGrasp/
├── assets/
│ ├── textures/
│ ├── union_ycb_unidex/
│ ├── franka/
│ ├── inspire_tac/
│ ├── ...
├── ckpt/
│ ├── inspire.pt
│ ├── ...
- Try our object-centric demonstration replay:
python run_rl_grasp.py task=grasp num_envs=16 task.env.asset.multiObjectList="union_ycb_unidex/example.yaml" +debug=test_demo_replay
You can see the Inspire hand grasping different objects with the same position, orientation, and hand pose.
- For Inspire hand, run:
python run_rl_grasp.py \
task=grasp \
train=PPOOneStep \
test=True \
num_envs=175 \
task.env.observationType="eefpose+objinitpose+objpcl" \
task.env.armController=pose \
task.env.asset.multiObjectList="union_ycb_unidex/union_ycb_debugset.yaml" \
task.env.randomizeTrackingReference=True \
task.env.randomizeGraspPose=True \
task.env.trackingReferenceFile=tasks/grasp_ref_inspire.pkl \
task.env.episodeLength=50 \
task.env.enablePointCloud=True \
train.params.is_vision=True \
checkpoint='ckpt/inspire.pt'
-
Use
headless=Trueon a headless machine. To evaluate on unseen object categories in DexGraspNet, settask.env.asset.multiObjectList="union_ycb_unidex/test_set_unseen_cat.yaml". -
Scripts for all hands are listed in
play_policy.sh
- For Inspire hand, run:
python -u run_rl_grasp.py \
task=grasp \
train=PPOOneStep \
hand=fr3_inspire_tac \
num_envs=7000 \
task.env.armController=pose \
task.env.trackingReferenceFile=tasks/grasp_ref_inspire.pkl \
task.env.trackingReferenceLiftTimestep=13 \
task.env.asset.multiObjectList="union_ycb_unidex/union_ycb_debugset.yaml" \
task.env.randomizeTrackingReference=True \
task.env.randomizeGraspPose=True \
task.env.resetDofPosRandomInterval=0.2 \
task.env.observationType="eefpose+objinitpose+objpcl" \
task.env.episodeLength=40 \
task.env.enablePointCloud=True \
train.params.is_vision=True \
task.env.enableRobotTableCollision=False
Checkpoints and tensorboard logs are saved in runs_ppo/.
-
Use
headless=Trueon a headless machine. To train on DexGraspNet benchmark (3200 objects), settask.env.asset.multiObjectList="union_ycb_unidex/train_set.yaml" headless=True num_envs=6400. -
Scripts for all hands are listed in
train.sh. Policies are expected to achieve high success rates after 2000 training iterations.
-
A headed machine with at least 20GB of GPU memory is required.
-
For Inspire hand, run:
python run_rl_grasp.py \
task=grasp \
train=PPOOneStep \
task.env.asset.multiObjectList="union_ycb_unidex/union_ycb_debugset.yaml" \
task.env.observationType="eefpose+objinitpose+objpcl" \
task.env.armController=pose \
task.env.randomizeTrackingReference=True \
task.env.randomizeGraspPose=True \
task.env.trackingReferenceFile=tasks/grasp_ref_inspire.pkl \
task.env.enablePointCloud=True \
train.params.is_vision=True \
task.env.render.enable=True \
task.env.render.camera_ids="[1,2]" \
task.env.render.data_type="rgb" \
task.env.render.randomize=True \
task.env.render.randomization_params.camera_pos="[-0.02,0.02]" \
task.env.render.randomization_params.camera_quat="[-0.02,0.02]" \
task.env.render.randomization_params.table_xyz="[0.05,0.02,0]" \
task.env.episodeLength=40 \
checkpoint='ckpt/inspire.pt' \
+debug=collect_real_dataset \
num_envs=175 \
+num_episodes=30000
Set +num_episodes to the total number of successful trajectories to collect.
The dataset will be saved in data/datasets/$robot_name_$datetime in LeRobot v2.0 format.
- You can use any imitation learning method to train an RGB-based policy on this dataset. To achieve sim-to-real deployment on your robot, you should revise the robot configurations, camera intrinsics and extrinsics, and table sizes to match your hardware settings.
Our codebase builds upon the following projects:
If you find our work useful, please consider citing us!
@article{yuan2025demograsp,
title={DemoGrasp: Universal Dexterous Grasping from a Single Demonstration},
author={Yuan, Haoqi and Huang, Ziye and Wang, Ye and Mao, Chuan and Xu, Chaoyi and Lu, Zongqing},
journal={arXiv preprint arXiv:2509.22149},
year={2025}
}