Authors: Takamuku, Shinya¹; Hosoda, Koh²; Asada, Minoru²

Source: Advanced Robotics, Volume 22, Number 10, 2008 , pp. 1143-1154(12)

Publisher: VSP, an imprint of Brill

Abstract:

The acquisition of object categories which underlie the human lexicon is a prerequisite for domestic robots to communicate with users in a human-like manner. The theory of J. J. Gibson inspires the approach to obtain shared categories through interaction with the shared environment, where explorative behaviors of infants play the role of obtaining distinctive features of objects to shape their categories. Although several existing studies have reproduced the exploratory behaviors of infants by robots to investigate their roles in acquiring such categories, those active categorization methods utilized static touches and the recognition tended to fail by changes of contact conditions. This paper introduces another possible approach to object categorization — object category acquisition by dynamic touch. Dynamic touch (e.g., shaking) provides the agent with the information of the whole object to enable quick and robust recognition. The amplitude spectrum of auditory data which humans obtain during shaking is found to be an effective feature for identifying the object categories of differing dynamics, e.g., rigid objects, paper materials and bottles of water, even though the objects within each category vary in size, shape, amount and contact conditions. Experimental results are given to show the validity of the proposed method and future issues are discussed.

Keywords: ACTIVE SENSING; CATEGORIZATION; DYNAMIC TOUCH; DYNAMIC SYSTEMS APPROACH; EMBODIMENT

Document Type: Research article

DOI: http://dx.doi.org/10.1163/156855308X324820

Affiliations: 1: JST Erato Asada Synergistic Intelligence Project, Department of Adaptive Machine Systems, Graduate School of Engineering, Osaka University, 2-1 Yamadaoka, Suita, Osaka 565-0871, Japan;, Email: shinya.takamuku@ams.eng.osaka-u.ac.jp 2: JST Erato Asada Synergistic Intelligence Project, Department of Adaptive Machine Systems, Graduate School of Engineering, Osaka University, 2-1 Yamadaoka, Suita, Osaka 565-0871, Japan

Publication date: 2008-06-01

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