DORA：面向灵巧机器人操作的物体可供性引导强化学习
DORA: Object Affordance-Guided Reinforcement Learning for Dexterous Robotic Manipulation
AI总结：
针对灵巧机器人操作的高维控制与语义交互难题，提出DORA物体可供性引导强化学习框架，通过可供性图约束、投票分类机制与融合奖励函数提升操作效率，三项任务成功率平均提升15.4%。
摘要：
由于控制空间的高维度性与物体交互的语义复杂性，灵巧机器人操作仍是机器人领域长期存在的挑战。本文提出一种物体可供性引导的强化学习框架，可让多指机器人手更高效地学习类人操作策略。该方法利用物体可供性图生成具有语义意义的抓取位姿候选，在训练中同时作为策略约束与先验。我们引入基于投票的抓取分类机制，确保抓取配置与物体可供性区域的功能对齐。此外，我们将这些约束整合到可泛化的RL流程中，并设计了融合可供性感知与任务特定目标的奖励函数。在立方体抓取、水壶抓取与提起、锤子使用三项操作任务上的实验结果表明，与基线方法相比，该可供性引导方法将任务成功率平均提升了15.4%。这些发现凸显了物体可供性先验在提升样本效率、学习可泛化且具备语义基础的操作策略方面的关键作用。更多细节请访问项目网站https://sites.google.com/view/dora-manip。
英文摘要：
Dexterous robotic manipulation remains a longstanding challenge in robotics due to the high dimensionality of control spaces and the semantic complexity of object interaction. In this paper, we propose an object affordance-guided reinforcement learning framework that enables a multi-fingered robotic hand to learn human-like manipulation strategies more efficiently. By leveraging object affordance maps, our approach generates semantically meaningful grasp pose candidates that serve as both policy constraints and priors during training. We introduce a voting-based grasp classification mechanism to ensure functional alignment between grasp configurations and object affordance regions. Furthermore, we incorporate these constraints into a generalizable RL pipeline and design a reward function that unifies affordance-awareness with task-specific objectives. Experimental results across three manipulation tasks - cube grasping, jug grasping and lifting, and hammer use - demonstrate that our affordance-guided approach improves task success rates by an average of 15.4% compared to baselines. These findings highlight the critical role of object affordance priors in enhancing sample efficiency and learning generalizable, semantically grounded manipulation policies. For more details, please visit our project website https://sites.google.com/view/dora-manip.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
