Motion control for humanoid robots based on the motion phase decision tree learning
Motion control for humanoid robots based on the motion phase decision tree learning
复制标题
基于运动阶段决策树学习的仿人机器人运动控制
DOI：
10.1109/mhs.2004.1421294
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发表时间：
2004
期刊：
影响因子：
--
通讯作者：
H. Itoh
中科院分区：
文献类型：
--
作者：
K. Kuwayama;S. Kato;T. Kunitachi;H. Itoh
仿人机器人由于其高自由度的连杆结构和对人类工作的可替代性，无论运动类型或环境如何，都需要复杂的运动控制技术。本文给出了一种基于概念学习的方法来解决这个问题。提出了一种基于运动相位的决策树学习的运动生成方法。该系统可以产生一个稳定的和抗翻滚的运动，将机器人转换到目标姿态。在实验中，目标运动是从椅子上站起来。仿人机器人HOAP-1完成了从椅子上站立的稳定和防翻滚动作。本文讨论了考虑运动相位的运动控制的有效性。
Humanoid robots, due to their link structure with high degree of freedom and the substitutability for human work, require a sophisticated motion control technique regardless of the type of motions or the environments. This paper gives a concept learning-based approach to this problem. We propose a motion generation method based on decision tree learning with motion phase. The system can generate a stable and anti-tumble motion which transforms the robot into a target posture. In experiment, the target motion are to stand up from a chair. Some stable and anti-tumble motions to stand up from a chair were performed by humanoid robot HOAP-1. In this paper, we discuss the validity of motion control considering motion phase.