The use and application of robotic arms in helping the aged and vulnerable persons are increasing gradually. In order to achieve safer and reliable human-robot interaction and its wider adoption, the requirements for the humanoid motion of robotic arms are becoming more stringent. This paper presents a humanoid motion planning method for a robotic arm based on the physics of human arm and reinforcement learning. Firstly, the humanoid motion rules are extracted by analyzing and learning the action data of human arm, which is collected using the VICON optical motion capture system. Then, according to the acquired features and rules, the corresponding reward functions are proposed and the humanoid motion training of the robotic arm is carried out by using the reinforcement learning based on Deep Deterministic Policy Gradient (DDPG) and Hindsight Experience Replay (HER) algorithm. Finally, the experiments are carried out to verify whether the robotic arm motions planned by the proposed approach are humanoid, and the observed results show its feasibility and effectiveness in planning the humanoid motion of the robotic arm.

机械臂在帮助老年人和弱势群体方面的使用和应用正在逐渐增加。为了实现更安全可靠的人机交互及其更广泛的应用，对机械臂仿人运动的要求越来越严格。本文提出了一种基于人类手臂物理和强化学习的机器人手臂仿人运动规划方法。首先，利用VICON光学运动捕捉系统对人体手臂的动作数据进行分析和学习，提取出仿人运动规则。然后，根据获得的特征和规则，提出了相应的奖励函数，并利用基于深度确定性策略梯度（DDPG）和事后经验回放（HER）算法的强化学习进行机械臂的仿人运动训练。最后，通过实验验证了所提出的方法所规划的机械臂运动是否是仿人的，观察结果表明其在规划机械臂仿人运动方面的可行性和有效性。