用强化学习实现双机械手精准交接物体，成功率94%。
Learning Dexterous Object Handover
- 设计基于双四元数的奖励函数，优化旋转对齐精度。
- 在未训练物体上仍达94%成功率，对扰动鲁棒性强。
- 适合需高精度协作的机器人场景，如家庭服务。
物体交接是人机交互中的重要技能。为使机器人在家庭等协作场景中有效工作，安全高效地接收与交付物体至关重要。本文展示利用强化学习（RL）实现双多指机械手间的灵巧物体交接。关键在于采用基于双四元数的新奖励函数以最小化旋转距离，优于欧拉角和旋转矩阵等传统表示方式。通过测试未包含在训练分布中的物体及交接过程中的扰动，验证了所训练策略的鲁棒性。结果表明，该策略在最佳情况下100次实验中总成功率达94%，展现出对新物体的良好适应能力；此外，在另一机械手移动时，最佳性能仅下降13.8%，证明其对真实世界常见扰动具有强鲁棒性。
原文摘要 · Abstract (English)
Object handover is an important skill that we use daily when interacting with other humans. To deploy robots in collaborative setting, like houses, being able to receive and handing over objects safely and efficiently becomes a crucial skill. In this work, we demonstrate the use of Reinforcement Learning (RL) for dexterous object handover between two multi-finger hands. Key to this task is the use of a novel reward function based on dual quaternions to minimize the rotation distance, which outperforms other rotation representations such as Euler and rotation matrices. The robustness of the trained policy is experimentally evaluated by testing w.r.t. objects that are not included in the training distribution, and perturbations during the handover process. The results demonstrate that the trained policy successfully perform this task, achieving a total success rate of 94% in the best-case scenario after 100 experiments, thereby showing the robustness of our policy with novel objects. In addition, the best-case performance of the policy decreases by only 13.8% when the other robot moves during the handover, proving that our policy is also robust to this type of perturbation, which is common in real-world object handovers.
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