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  Active In-Hand Object Recognition on a Humanoid Robot

Browatzki, B., Tikhanoff, V., Metta, G., Bülthoff, H., & Wallraven, C. (2014). Active In-Hand Object Recognition on a Humanoid Robot. IEEE Transactions on Robotics, 30(5), 1260-1269. doi:10.1109/TRO.2014.2328779.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0027-7FA9-A Version Permalink: http://hdl.handle.net/21.11116/0000-0001-275D-C
Genre: Journal Article

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Browatzki, B1, 2, Author              
Tikhanoff, V, Author
Metta, G, Author
Bülthoff, HH1, 2, Author              
Wallraven, C, Author              
Affiliations:
1Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497797              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: For any robot, the ability to recognize and manipulate unknown objects is crucial to successfully work in natural environments. Object recognition and categorization is a very challenging problem, as 3-D objects often give rise to ambiguous, 2-D views. Here, we present a perception-driven exploration and recognition scheme for in-hand object recognition implemented on the iCub humanoid robot. In this setup, the robot actively seeks out object views to optimize the exploration sequence. This is achieved by regarding the object recognition problem as a localization problem. We search for the most likely viewpoint position on the viewsphere of all objects. This problem can be solved efficiently using a particle filter that fuses visual cues with associated motor actions. Based on the state of the filter, we can predict the next best viewpoint after each recognition step by searching for the action that leads to the highest expected information gain. We conduct extensive evaluations of the proposed system in simulation as well as on the actual robot and show the benefit of perception-driven exploration over passive, vision-only processes at discriminating between highly similar objects. We demonstrate that objects are recognized faster and at the same time with a higher accuracy.

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 Dates: 2014-10
 Publication Status: Published in print
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 Identifiers: DOI: 10.1109/TRO.2014.2328779
BibTex Citekey: BrowatzkiTMBW2014
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Title: IEEE Transactions on Robotics
Source Genre: Journal
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Pages: - Volume / Issue: 30 (5) Sequence Number: - Start / End Page: 1260 - 1269 Identifier: -