| CARVIEW |
GenDexHand: Generative Simulation for Dexterous Hands
* Equal Contribution
‡ Corresponding authors
Abstract
Data scarcity remains a fundamental bottleneck for embodied intelligence. Existing approaches use large language models (LLMs) to automate gripper-based simulation generation, but they transfer poorly to dexterous manipulation, which demands more specialized environment design. Meanwhile, dexterous manipulation tasks are inherently more difficult due to their higher degrees of freedom. Massively generating feasible and trainable dexterous hand tasks remains an open challenge. To this end, we present GenDexHand, a generative simulation pipeline that autonomously produces diverse robotic tasks and environments for dexterous manipulation. GenDexHand introduces a closed-loop refinement process that adjusts object placements and scales based on vision-language model (VLM) feedback, substantially improving the average quality of generated environments. Each task is further decomposed into sub-tasks to enable sequential reinforcement learning, reducing training time and increasing success rates. Our work provides a viable path toward scalable training of diverse dexterous hand behaviors in embodied intelligence by offering a simulation-based solution to synthetic data generation.
GenDexHand Overview
Overview of the GenDexHand pipeline for task generation. The process consists of four stages: Environment Proposal, Environment Creation, MLLM Refinement, and Trajectory Generation. Embodied assets and object assets are first provided to the Generator to produce an environment proposal. The simulator then renders multi-view images of the proposed scene, which are refined using an MLLM. Finally, the refined environment and proposal are combined to generate the resulting dexterous hand trajectory.
Generation result
Experiment
TASK QUALITY OF GENDEXHAND
| Method | all-MiniLM-L6-v2 | all-mpnet-base-v2 | all-distilroberta-v1 |
|---|---|---|---|
| GenDexHand | 0.2880 | 0.2836 | 0.3156 |
| RoboGen | 0.1906 | 0.2174 | 0.1952 |
| RoboTwin | 0.3237 | 0.3589 | 0.3945 |
| Bi-DexHands | 0.2212 | 0.2110 | 0.2030 |
| Meta-World | 0.5213 | 0.5335 | 0.5981 |
EFFICIENCY OF POLICY LEARNING
BibTeX
@misc{chen2025gendexhandgenerativesimulationdexterous,
title={GenDexHand: Generative Simulation for Dexterous Hands},
author={Feng Chen and Zhuxiu Xu and Tianzhe Chu and Xunzhe Zhou and Li Sun and Zewen Wu and Shenghua Gao and Zhongyu Li and Yanchao Yang and Yi Ma},
year={2025},
eprint={2511.01791},
archivePrefix={arXiv},
primaryClass={cs.RO},
url={https://arxiv.org/abs/2511.01791},
}