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

翻译：实现人类级别的操控需要能够进行复杂物体交互的灵巧机械手。进一步推进此类能力需要标准化的基准来系统评估。然而，现有的灵巧操作基准缺乏能体现灵巧手相对于平行夹爪独特操控能力的任务，也缺少全面的评估流程。本文提出DexJoCo，一个面向任务导向的灵巧操作基准与工具包，包含11个功能导向型任务，用于评估工具使用、双手协调、长时域执行和推理能力。我们开发了低成本数据采集系统，收集了这些任务上的1100条轨迹，并支持域随机化以评估鲁棒性。我们在多样化设置（包括视觉与动力学随机化、多任务训练和动作头适配）下对现代模型进行了基准测试。通过广泛的实证分析，我们识别出当前灵巧操作策略中的若干重要见解与常见局限，揭示了灵巧手机器人学习未来研究的关键挑战。项目页面：https://dexjoco.github.io