Dexterous hands exhibit significant potential for complex real-world grasping tasks. While recent studies have primarily focused on learning policies for specific robotic hands, the development of a universal policy that controls diverse dexterous hands remains largely unexplored. In this work, we study the learning of cross-embodiment dexterous grasping policies using reinforcement learning (RL). Inspired by the capability of human hands to control various dexterous hands through teleoperation, we propose a universal action space based on the human hand's eigengrasps. The policy outputs eigengrasp actions that are then converted into specific joint actions for each robot hand through a retargeting mapping. We simplify the robot hand's proprioception to include only the positions of fingertips and the palm, offering a unified observation space across different robot hands. Our approach demonstrates an 80% success rate in grasping objects from the YCB dataset across four distinct embodiments using a single vision-based policy. Additionally, our policy exhibits zero-shot generalization to two previously unseen embodiments and significant improvement in efficient finetuning. For further details and videos, visit our project page https://sites.google.com/view/crossdex.

翻译：灵巧手在复杂现实抓取任务中展现出巨大潜力。尽管近期研究主要集中于为特定机械手学习控制策略，但开发能够控制多种灵巧手的通用策略仍属未充分探索的领域。本研究利用强化学习探索跨具身灵巧抓取策略的学习机制。受人类通过遥操作控制各类灵巧手的能力启发，我们提出基于人手特征抓取模式的通用动作空间。策略输出的特征抓取动作通过重定向映射转换为各机械手的特定关节动作。我们将机械手本体感知简化为仅包含指尖与手掌位置信息，从而构建跨不同机械手的统一观测空间。该方法在YCB数据集物体抓取任务中，使用单一视觉策略在四种不同具身上实现了80%的成功率。此外，该策略对两种未见具身表现出零样本泛化能力，并在高效微调中取得显著提升。更多细节与视频请访问项目页面 https://sites.google.com/view/crossdex。