基于多级核化运动基元的人机交递轨迹模仿学习

Learning Human-robot Handover Trajectory by Imitation Based on Multi-level Kernelized Movement Primitives

针对人机交递任务中的机器人轨迹再现与泛化问题,提出一种基于多级核化运动基元的人机联合轨迹单例模仿学习算法。通过将一个空间子区域中学习到的人手与机械臂末端位置之间的非线性关系迁移至其他子区域,实现人机交递技能对整体交互工作空间的覆盖;对机械臂末端动作轨迹长度进行建模与调制,使得算法能在轨迹重现过程中适应机械臂末端轨迹的长度变化。在UR5机器人上的实验表明,相较于传统核化运动基元算法,多级核化运动基元算法避免了对完整人手位置空间区域进行等密度采样,消除了在对某一子区域内人手位置进行推理时其他区域采样点的干扰,将推理平均误差由7.11:cm降低至1.85:cm,预测平均时间由0.138:s降低至0.015:s,提高了预测精度和实时性,针对具有尺度差异的人机联合轨迹样本,将交递成功率由13.3%提升至100%,提升了算法的学习与适应能力。

Aiming at the problem of robot trajectories reproduction and generalization for human-robot handover tasks,a one-shot imitation learning method for human-robot joint trajectories is proposed based on multi-level kernelized movement primitives.By transferring the non-linear relationship between the positions of the human hand and the robot end-effector,which is learned in a spatial sub-region,to other sub-regions,the human-robot handover skills can cover the overall interaction workspace.By modeling and modulating the length of robot end-effector motion trajectories,the algorithm can adapt to the length variance of robot end-effector trajectory during trajectory reproduction.Experiments on a UR5 robot show that,compared with classical kernelized movement primitives algorithm,the proposed multi-level kernelized movement primitives algorithm avoids the uniform sampling of the complete human hand position space and eliminates the interference of sampling points from other sub-regions during inference procedure of hand position in a certain sub-region.Therefore,the average inference error is reduced from 7.11 cm to 1.85 cm,and the average inference time consumption is shortened from 0.138 s to 0.015 s,which achieves higher accuracy and better real-time performance.The proposed algorithm improves handover success rate from 13.3%to 100%on the hand-robot joint trajectories with significant scale variance,which outperforms classical kernelized movement primitives algorithm in the learning ability and adaptability.