HANDFUL: 有意识资源利用的顺序抓取-条件灵活操作
HANDFUL: Sequential Grasp-Conditioned Dexterous Manipulation with Resource Awareness
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- Northwestern University（西北大学）
- University of Southern California（南加州大学）
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中文总结 AI 辅助
本文提出HANDFUL框架，通过资源感知抓取提升多任务操作的灵活性和鲁棒性，结合仿真与现实验证其有效性。
中文摘要 AI 辅助
灵活的机器人手部提供了多功能操作的丰富机会，其中机器人必须在保持已抓取物体控制的同时依次执行多个技能。大多数现有研究集中在单对象、单技能任务上。相反，我们的见解是许多顺序任务需要资源感知的抓取，以保留手指用于未来动作。本文研究了顺序抓取-条件灵活操作，其中机器人首先抓取一个物体，然后在保持初始抓取的同时执行第二个不同的操作子任务。我们引入HANDFUL，一种学习框架，将手指使用视为有限资源，并通过手指级接触奖励鼓励探索资源感知的抓取。这些抓取随后通过基于课程的策略学习选择用于下游任务。我们进一步提出了HANDFUL-Bench仿真基准，引入了多个第二子任务目标（如推动、拉拽和按压）的顺序灵活操作任务，在共享的抓取-条件设置下。广泛的仿真结果表明，优先考虑资源感知的抓取比在尝试第二个子任务前贪婪优化初始抓取更能提高第二子任务的成功率和鲁棒性。我们还验证了在现实中的灵活LEAP手上的方法。总的来说，这项工作确立了资源感知抓取规划作为多功能灵活操作的关键原则。补充材料可在我们的网站上获得：https://handful-dex.github.io。
英文摘要
Dexterous robot hands offer rich opportunities for multifunctional manipulation, where a robot must execute multiple skills in sequence while maintaining control over previously grasped objects. Most prior work in dexterous manipulation focuses on single-object, single-skill tasks. In contrast, our insight is that many sequential tasks require resource-aware grasps that conserve fingers for future actions. In this paper, we study sequential grasp-conditioned dexterous manipulation, where a robot first grasps an object and then performs a second, distinct manipulation subtask while preserving the initial grasp. We introduce HANDFUL, a learning framework that models finger usage as a limited resource and encourages exploration of resource-aware grasps through finger-level contact rewards. These grasps are subsequently selected for downstream tasks via curriculum-based policy learning. We further propose HANDFUL-Bench, a simulation benchmark that introduces sequential dexterous manipulation tasks across multiple secondsubtask objectives, including pushing, pulling, and pressing, under a shared grasp-conditioned setup. Extensive simulation results demonstrate that prioritizing resource-aware grasps improves second-subtask success and robustness compared to a baseline that greedily optimizes the initial grasp before attempting the second subtask. We additionally validate our approach on a real dexterous LEAP hand. Together, this work establishes resource-aware grasp planning as a key principle for multifunctional dexterous manipulation. Supplementary material is available on our website: https://handful-dex.github.io.
