DexJoCo: A Benchmark and Toolkit for Task-Oriented Dexterous Manipulation on MuJoCo
摘要
Achieving human-level manipulation requires dexterous robotic hands capable of complex object interactions. Advancing such capabilities further demands standardized benchmarks for systematic evaluation. However, existing dexterous benchmarks lack tasks that reflect the unique manipulation capabilities of dexterous hands over parallel grippers, as well as comprehensive evaluation pipelines. In this paper, we present DexJoCo, a benchmark and toolkit for task-oriented dexterous manipulation, comprising 11 functionally grounded tasks that evaluate tool-use, bimanual coordination, long-horizon execution, and reasoning. We develop a low-cost data collection system and collect 1.1K trajectories across these tasks, with support for domain randomization to assess robustness. We benchmark modern models under diverse settings, including visual and dynamics randomization, multi-task training, and action-head adaptation. Through extensive empirical analysis, we identify several important insights and common limitations of current policies in dexterous manipulation, highlighting key challenges for future research in dexterous hand robot learning. Project page available at: https://dexjoco.github.io
关键词
引用
@article{arxiv.2605.16257,
title = {DexJoCo: A Benchmark and Toolkit for Task-Oriented Dexterous Manipulation on MuJoCo},
author = {Hanwen Wang and Weizhi Zhao and Xiangyu Wang and Siyuan Huang and He Lin and Boyuan Zheng and Rongtao Xu and Gang Wang and Yao Mu and He Wang and Lue Fan and Hongsheng Li and Zhaoxiang Zhang and Tieniu Tan},
journal= {arXiv preprint arXiv:2605.16257},
year = {2026}
}
备注
8 pages, 6 figures, project page is available at: https://dexjoco.github.io