Learning meaningful interactions from repetitious motion patterns

Koichi Ogawara, Yasufumi Tanabe, Ryo Kurazume, Tsutomu Hasegawa

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

2 Citations (Scopus)

Abstract

In this paper, we propose a method for estimating meaningful actions from long-term observation of everyday manipulation tasks without prior knowledge as part of an action understanding framework for life support robotic systems. The target task is defined as a sequence of interactions between objects. An interaction that appears many times is assumed to be meaningful and repetitious relative motion patterns are detected from trajectories of multiple objects. The main contribution is that the problem is formulated as a combinatorial optimization problem with two parameters, target object labels and correspondences on similar motion patterns, and is solved using local and global Dynamic Programming (DP) in polynomial time O(N logN), where N is a total amount of data. The proposed method is evaluated against manipulation tasks using everyday objects such as a cup and a tea-pot.

Original languageEnglish
Title of host publication2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS
Pages3350-3355
Number of pages6
DOIs
Publication statusPublished - Dec 1 2008
Event2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS - Nice, France
Duration: Sep 22 2008Sep 26 2008

Publication series

Name2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS

Other

Other2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS
CountryFrance
CityNice
Period9/22/089/26/08

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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