一种用于机械臂拟人化控制的学习框架

A Learning Framework for Controlling Robotic Manipulators with Human-like Actions

赋予机器人拟人化的动作可以使机器人的行为更具可解释性和可预测性,可以显著提升人机协作任务的质量和安全性。本文提出一个“人在回路”学习框架,从人类遥操作示教中学习拟人化姿态特征,并将学习到的特征模型应用于冗余机械臂的控制中产生拟人化动作。在模型训练过程中采用“人在回路”的在线再标注方法,克服了协变量偏移问题,将总示教时间缩减至10 min以内。人机姿态对比和动态轨迹跟踪任务实验验证了该框架训练得到的拟人化控制方法的有效性。用户评价测试表明,使用拟人化姿态约束的机械臂在主观体验方面对用户更加友好,作为共同作业的工具更容易被非专业用户所接受。

Endowing robotic manipulators with human-like actions can make their behaviours more explainable and predictable,which improves the quality and safety of human-robot collaboration(HRC).A human-in-the-loop learning framework is proposed to learn the human-like posture features from human teleoperation demonstration.The learnt feature model can be used to generate human-like actions on the redundant manipulators.Moreover,an online relabeling approach is adopted in the training session to address the covariate shift problem,which reduces the total demonstration time to 10 min or less.The effectiveness of the human-like control method trained by the proposed framework is validated through comparing the posture between the human and the robot,and tracking the dynamic trajectories.A user study shows that,manipulators controlled with the proposed human-like posture constraint method are more friendly to the users in subjective feelings,and more acceptable as co-operative tools for non-professional users.