Purpose

Imitation learning is a powerful tool for planning the trajectory of robotic end-effectors in Cartesian space. Present methods can adapt the trajectory to the obstacle; however, the solutions may not always satisfy users, whereas it is hard for a nonexpert user to teach the robot to avoid obstacles in time as he/she wishes through demonstrations. This paper aims to address the above problem by proposing an approach that combines human supervision with the kernelized movement primitives (KMP) model.

Design/methodology/approach

This approach first extracts the reference database used to train KMP from demonstrations by using Gaussian mixture model and Gaussian mixture regression. Subsequently, KMP is used to modulate the trajectory of robotic end-effectors in real time based on feedback from its interaction with humans to avoid obstacles, which benefits from a novel reference database update strategy. The user can test different obstacle avoidance trajectories in the current task until a satisfactory solution is found.

Findings

Experiments performed with the KUKA cobot for obstacle avoidance show that this approach can adapt the trajectories of the robotic end-effector to the user’s wishes in real time, including trajectories that the robot has already passed and has not yet passed. Simulation comparisons also show that it exhibits better performance than KMP with the original reference database update strategy.

Originality/value

An interactive learning approach based on KMP is proposed and verified, which not only enables users to plan the trajectory of robotic end-effectors for obstacle avoidance more conveniently and efficiently but also provides an effective idea for accomplishing interactive learning tasks under constraints.

中文翻译：

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基于KMP的机器人轨迹自适应避障交互式学习方法

目的

模仿学习是规划笛卡尔空间中机器人末端执行器轨迹的强大工具。目前的方法可以使轨迹适应障碍物；然而，这些解决方案并不总是能让用户满意，而对于非专业用户来说，很难通过演示教会机器人按照自己的意愿及时避开障碍物。本文旨在通过提出一种将人类监督与内核化运动原语（KMP）模型相结合的方法来解决上述问题。

设计/方法论/途径

该方法首先使用高斯混合模型和高斯混合回归从演示中提取用于训练 KMP 的参考数据库。随后，KMP 用于根据机器人末端执行器与人类交互的反馈实时调节机器人末端执行器的轨迹，以避免障碍物，这得益于新颖的参考数据库更新策略。用户可以在当前任务中测试不同的避障轨迹，直到找到满意的解决方案。

发现

使用 KUKA 协作机器人进行的避障实验表明，这种方法可以使机器人末端执行器的轨迹实时适应用户的意愿，包括机器人已经通过和尚未通过的轨迹。仿真比较还表明，与采用原始参考数据库更新策略的KMP相比，它表现出更好的性能。

原创性/价值

提出并验证了一种基于KMP的交互式学习方法，不仅使用户能够更方便、高效地规划机器人末端执行器的轨迹进行避障，而且为在约束条件下完成交互式学习任务提供了有效的思路。