NDLI: Object recognition using visuo-affordance maps

Content Provider	IEEE Xplore Digital Library
Author	Gijsberts, A. Tommasi, T. Metta, G. Caputo, B.
Copyright Year	2010
Description	Author affiliation: Idiap Research Institute, Centre Du Parc, Rue Marconi 19, CH-1920 Martigny, Switzerland (Tommasi, T.; Caputo, B.) \|\| Department of Robotics, Brain and Cognitive Sciences, Italian Institute of Technology, Via Morego 30, 16163 Genoa, Italy (Gijsberts, A.; Metta, G.)
Abstract	One of the major challenges in developing autonomous systems is to make them able to recognize and categorize objects robustly. However, the appearance-based algorithms that are widely employed for robot perception do not explore the functionality of objects, described in terms of their affordances. These affordances (e.g., manipulation, grasping) are discriminative for object categories and are important cues for reliable robot performance in everyday environments. In this paper, we propose a strategy for object recognition that integrates both visual appearance and grasp affordance features. Following previous work, we hypothesize that additional grasp information improves object recognition, even if we reconstruct the grasp modality from visual features using a mapping function. We considered two different representations for the grasp modality: (1) motor information of the hand posture while grasping and (2) a more general grasp affordance descriptor. Using a multi-modal classifier we show that having real grasp information significantly boost object recognition. This improvement is preserved, although to a lesser extent, if the grasp modality is reconstructed using the mapping function.
Starting Page	1572
Ending Page	1578
File Size	1025282
Page Count	7
File Format	PDF
ISBN	9781424466740
ISSN	21530866
e-ISBN	9781424466764
DOI	10.1109/IROS.2010.5649238
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-10-18
Publisher Place	Taiwan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Visualization Training Object recognition Image reconstruction Kernel Grasping Robots
Content Type	Text
Resource Type	Article

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