构建了评估灵巧手操作能力的基准与工具集
DexJoCo: A Benchmark and Toolkit for Task-Oriented Dexterous Manipulation on MuJoCo
- 设计11项功能任务，覆盖工具使用与双臂协作
- 收集1100条轨迹，支持随机化测试鲁棒性
- 适合研究灵巧操作与机器人学习的团队使用
实现人类级操作需具备复杂物体交互能力的灵巧机械手。推动该能力发展需标准化基准进行系统评估。然而，现有灵巧手基准缺乏体现其独特优势的任务，也缺少完整的评估流程。本文提出DexJoCo，一个面向任务导向灵巧操作的基准与工具集，包含11个功能型任务，涵盖工具使用、双臂协调、长时序执行与推理能力评估。我们搭建低成本数据采集系统，收集了1100条跨任务轨迹，并支持领域随机化以评估模型鲁棒性。在多种设置下（视觉与动力学随机化、多任务训练、动作头适配）对现代模型进行基准测试。通过大量实证分析，揭示当前策略在灵巧操作中的若干重要发现与普遍局限，指明未来研究的关键挑战。项目页面：https://dexjoco.github.io
原文摘要 · Abstract (English)
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
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