用物体功能引导强化学习，让机械手更高效地学会类人操作。
DORA: Object Affordance-Guided Reinforcement Learning for Dexterous Robotic Manipulation
- 基于物体功能图生成有意义的抓取姿势候选，作为训练约束和先验。
- 在三个任务中平均提升15.4%的成功率，显著改善样本效率。
- 适合研究灵巧操作、强化学习与具身智能的开发者参考。
灵巧机器人操作因控制空间高维性和物体交互语义复杂性而长期面临挑战。本文提出一种基于物体功能引导的强化学习框架，使多指机械手更高效地学习类人操作策略。通过利用物体功能图，方法生成语义上合理的抓取姿态候选，作为训练中的策略约束与先验。设计基于投票的抓取分类机制，确保抓取配置与物体功能区域的功能对齐。进一步将这些约束融入可泛化的强化学习流程，并构建统一了功能感知与任务目标的奖励函数。在立方体抓取、水壶抓取与举起、锤子使用三项任务上的实验结果表明，相比基线方法，本方法平均提升任务成功率15.4%。研究结果凸显了物体功能先验在提升样本效率和学习可泛化、语义一致的操作策略中的关键作用。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。