Effective motion learning for a flexible-joint robot using motor babbling

Kuniyuki Takahashi, Tetsuya Ogata, Hiroki Yamada, Hadi Tjandra, Shigeki Sugano

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)

Abstract

We propose a method for realizing effective dynamic motion learning in a flexible-joint robot using motor babbling. Flexible-joint robots have recently attracted attention because of their adaptiveness, safety, and, in particular, dynamic motions. It is difficult to control robots that require dynamic motion. In past studies, attractors and oscillators were designed as motion primitives of an assumed task in advance. However, it is difficult to adapt to unintended environmental changes using such methods. To overcome this problem, we use a recurrent neural network (RNN) that does not require predetermined parameters. In this research, we propose a method for facilitating effective learning. First, a robot learns simple motions via motor babbling, acquiring body dynamics using a recurrent neural network (RNN). Motor babbling is the process of movement that infants use to acquire their own body dynamics during their early days. Next, the robot learns additional motions required for a target task using the acquired body dynamics. For acquiring these body dynamics, the robot uses motor babbling with its redundant flexible joints to learn motion primitives. This redundancy implies that there are numerous possible motion patterns. In comparison to a basic learning task, the motion primitives are simply modified to adjust to the task. Next, we focus on the types of motions used in motor babbling. We classify the motions into two motion types, passive motion and active motion. Passive motion involves inertia without any torque input, whereas active motion involves a torque input. The robot acquires body dynamics from the passive motion and a means of torque generation from the active motion. As a result, we demonstrate the importance of performing prior learning via motor babbling before learning a task. In addition, task learning is made more efficient by dividing the motion into two types of motor babbling patterns.

Original languageEnglish
Title of host publicationIROS Hamburg 2015 - Conference Digest
Subtitle of host publicationIEEE/RSJ International Conference on Intelligent Robots and Systems
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2723-2728
Number of pages6
ISBN (Electronic)9781479999941
DOIs
Publication statusPublished - 2015 Dec 11
EventIEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2015 - Hamburg, Germany
Duration: 2015 Sep 282015 Oct 2

Publication series

NameIEEE International Conference on Intelligent Robots and Systems
Volume2015-December
ISSN (Print)2153-0858
ISSN (Electronic)2153-0866

Other

OtherIEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2015
CountryGermany
CityHamburg
Period15/9/2815/10/2

Keywords

  • Dynamics
  • Neurons
  • Oscillators
  • Recurrent neural networks
  • Robot sensing systems
  • Torque

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Computer Vision and Pattern Recognition
  • Computer Science Applications

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