让机器人更安全高效地与人协作，自动避障并减少重试。
A Task-Efficient Reinforcement Learning Task-Motion Planner for Safe Human-Robot Cooperation
- 用强化学习选任务，结合实时避障规划
- 减少失败重试和重规划次数
- 适合需要高安全与效率的协作场景
在人机协作（HRC）环境中，安全与效率是评估机器人性能的两大核心指标。然而，安全机制常导致任务效率下降，因人类干预引发机器人的回退动作和目标失败，频繁的路径重规划则增加计算负载与出错概率。本文提出一种混合强化学习（RL）规划框架，包含交互式运动规划器与RL任务规划器。任务规划器基于运动规划器的反馈，选择统计上安全且高效的任务序列；运动规划器通过检测人类手臂动作，在原路径失效时动态生成新路径，确保执行过程无碰撞。直观上，RL代理学会规避危险任务，而运动规划器保障所选任务的安全性。该框架在仿真与真实机械臂上验证，对比硬编码任务-运动规划方法，结果表明：1）可在关节级与任务级响应不确定的人类动作；2）减少重复失败的目标指令次数；3）降低总重规划请求量。
原文摘要 · Abstract (English)
In a Human-Robot Cooperation (HRC) environment, safety and efficiency are the two core properties to evaluate robot performance. However, safety mechanisms usually hinder task efficiency since human intervention will cause backup motions and goal failures of the robot. Frequent motion replanning will increase the computational load and the chance of failure. In this paper, we present a hybrid Reinforcement Learning (RL) planning framework which is comprised of an interactive motion planner and a RL task planner. The RL task planner attempts to choose statistically safe and efficient task sequences based on the feedback from the motion planner, while the motion planner keeps the task execution process collision-free by detecting human arm motions and deploying new paths when the previous path is not valid anymore. Intuitively, the RL agent will learn to avoid dangerous tasks, while the motion planner ensures that the chosen tasks are safe. The proposed framework is validated on the cobot in both simulation and the real world, we compare the planner with hard-coded task motion planning methods. The results show that our planning framework can 1) react to uncertain human motions at both joint and task levels; 2) reduce the times of repeating failed goal commands; 3) reduce the total number of replanning requests.
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