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Active Object Recognition on a Humanoid Robot

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons83834

Browatzki,  B
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons83839

Tikhanoff V, Metta G, Bülthoff,  HH
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons84298

Wallraven,  C
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Zitation

Browatzki, B., Tikhanoff V, Metta G, Bülthoff, H., & Wallraven, C. (2012). Active Object Recognition on a Humanoid Robot. In IEEE International Conference on Robotics and Automation (ICRA 2012) (pp. 2021-2028). Piscataway, NJ, USA: IEEE.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0013-B77E-5
Zusammenfassung
Interaction with its environment is a key requisite for a humanoid robot. Especially the ability to recognize and manipulate unknown objects is crucial to successfully work in natural environments. Visual object recognition, however, still remains a challenging problem, as three-dimensional objects often give rise to ambiguous, two-dimensional views. Here, we propose a perception-driven, multisensory exploration and recognition scheme to actively resolve ambiguities that emerge at certain viewpoints. We define an efficient method to acquire two-dimensional views in an object-centered task space and sample characteristic views on a view sphere. Information is accumulated during the recognition process and used to select actions expected to be most beneficial in discriminating similar objects. Besides visual information we take into account proprioceptive information to create more reliable hypotheses. Simulation and real-world results clearly demonstrate the efficiency of active, multisensory exploration over passive, visiononly recognition methods.