Online learning of task-specific dynamics for periodic tasks
Online learning of task-specific dynamics for periodic tasks
复制标题
在线学习周期性任务的特定任务动态
DOI：
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
A. Ude
中科院分区：
文献类型：
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作者：
T. Petrič;A. Gams;L. Žlajpah;A. Ude
在本文中，我们解决了在确保周期性任务的机器人行为符合要求的同时进行准确的轨迹跟踪的问题。我们提出了一种在线学习特定任务动力学的方法，即特定于任务的运动轨迹和相应的力/力矩轮廓。提出的控制框架是一个多步骤的过程，在第一步中，人类导师展示如何执行所需的周期性任务。采用基于自适应频率振荡器和动态运动基元相结合的状态估计器提取运动轨迹。第二步，在人的监督下，在受控环境中准确执行运动轨迹。在这一步中，机器人精确地跟踪获取的运动轨迹，使用高反馈增益来确保准确跟踪。因此，它可以学习相应的力/力矩分布，即特定于任务的动力学。最后，在第三步中，使用学习的前馈任务特定的动态模型来执行运动，从而允许较低的位置反馈增益，这意味着遵从机器人的行为。因此，与人类或环境互动是安全的。在一台执行物体操作和曲柄转动的库卡LRW机器人上对该方法进行了评估。
In this paper we address the problem of accurate trajectory tracking while ensuring compliant robotic behaviour for periodic tasks. We propose an approach for on-line learning of task-specific dynamics, i.e. task specific movement trajectories and corresponding force/torque profiles. The proposed control framework is a multi-step process, where in the first step a human tutor shows how to perform the desired periodic task. A state estimator based on an adaptive frequency oscillator combined with dynamic movement primitives is employed to extract movement trajectories. In the second step, the movement trajectory is accurately executed in the controlled environment under human supervision. In this step, the robot is accurately tracking the acquired movement trajectory, using high feedback gains to ensure accurate tracking. Thus it can learn the corresponding force/torque profiles, i. e. task-specific dynamics. Finally, in the third step, the movement is executed with the learned feedforward task-specific dynamic model, allowing for low position feedback gains, which implies compliant robot behaviour. Thus, it is safe for interaction with humans or the environment. The proposed approach was evaluated on a Kuka LRW robot performing object manipulation and crank turning.