基于强化学习的缝合针双手抓取快速运动规划
Bimanual Regrasping for Suture Needles using Reinforcement Learning for Rapid Motion Planning
摘要：
重新抓持缝合针是一个重要但耗时的过程。为了提高重新夹持的效率，先前的工作要么设计一个特定于任务的机制，要么将夹持器引导到一些特定的拾取点，以便正确地夹持针。然而，当工作空间改变时，这些方法通常无法部署。因此，在这项工作中，我们提出了快速的轨迹生成双手针重握通过强化学习（RL）。从采样为基础的运动规划算法的演示被纳入加快学习。此外，我们提出了以自我为中心的状态和动作空间，这个双手规划问题，其中的参考帧是在末端执行器，而不是一些固定的框架。因此，学习的策略可以直接应用于任何可行的机器人配置。仿真实验表明，该算法的单次规划成功率为97%，平均规划时间为0.0212 s，优于目前广泛使用的其他运动规划算法。对于真实世界的实验，如果从RGB图像重建针位姿，则成功率为73.3%，规划时间为0.0846 s，运行时间为5.1454 s。如果预先知道针位，则成功率为90.5%，规划时间为0.0807 s，运行时间为2.8801 s。
英文摘要：
Regrasping a suture needle is an important yet time-consuming process in suturing. To bring efficiency into regrasping, prior work either designs a task-specific mechanism or guides the gripper toward some specific pick-up point for proper grasping of a needle. Yet, these methods are usually not deployable when the working space is changed. Therefore, in this work, we present rapid trajectory generation for bimanual needle regrasping via reinforcement learning (RL). Demonstrations from a sampling-based motion planning algorithm is incorporated to speed up the learning. In addition, we propose the ego-centric state and action spaces for this bimanual planning problem, where the reference frames are on the end-effectors instead of some fixed frame. Thus, the learned policy can be directly applied to any feasible robot configuration. Our experiments in simulation show that the success rate of a single pass is 97%, and the planning time is 0.0212s on average, which outperforms other widely used motion planning algorithms. For the real-world experiments, the success rate is 73.3% if the needle pose is reconstructed from an RGB image, with a planning time of 0.0846s and a run time of 5.1454s. If the needle pose is known beforehand, the success rate becomes 90.5%, with a planning time of 0.0807s and a run time of 2.8801s.
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。
