Humanoid robot posture-control learning in real-time based on human sensorimotor learning ability
Humanoid robot posture-control learning in real-time based on human sensorimotor learning ability
复制标题
基于人类感觉运动学习能力的仿人机器人姿态控制实时学习
DOI：
10.1109/icra.2013.6631340
复制
发表时间：
2013
期刊：
影响因子：
--
通讯作者：
J. Babič
中科院分区：
文献类型：
--
作者：
L. Peternel;J. Babič
在本文中，我们提出了一个系统能够教人形机器人的新技能，在实时。该系统旨在简化机器人控制，并在人与机器人之间提供自然和直观的交互。该系统的关键要素是利用人类的感觉运动学习能力，其中人类演示者学习如何以与人类适应各种日常任务相同的方式操作机器人。所提出的系统的另一个关键方面是，机器人学习任务的同时，人类正在操作机器人。这使得机器人的控制能够在演示期间逐渐从人转移到机器人。控制是根据模仿任务的准确性来转移的。我们证明了我们的方法，使用一个实验，其中一个人类演示教一个人形机器人如何保持姿态稳定的扰动的存在。为了提供适当的反馈信息的机器人的姿势稳定的人类感觉运动系统，我们利用了一个定制的触觉接口。为了使机器人能够吸收演示的技能，我们使用了局部加权投影回归机器学习方法。一种新的方法，逐步转移控制责任从人类的增量构建的自主机器人控制器。
In this paper we propose a system capable of teaching humanoid robots new skills in real-time. The system aims to simplify the robot control and to provide a natural and intuitive interaction between the human and the robot. The key element of the system is exploitation of the human sensorimotor learning ability where a human demonstrator learns how to operate a robot in the same fashion as humans adapt to various everyday tasks. Another key aspect of the proposed system is that the robot learns the task simultaneously while the human is operating the robot. This enables the control of the robot to be gradually transferred from the human to the robot during the demonstration. The control is transferred based on the accuracy of the imitated task. We demonstrated our approach using an experiment where a human demonstrator taught a humanoid robot how to maintain the postural stability in the presence of the perturbations. To provide the appropriate feedback information of the robot's postural stability to the human sensorimotor system, we utilized a custom-built haptic interface. To absorb the demonstrated skill by the robot, we used Locally Weighted Projection Regression machine learning method. A novel approach was implemented to gradually transfer the control responsibility from the human to the incrementally built autonomous robot controller.