Active end-effector pose selection for tactile object recognition through Monte Carlo tree search
| Autor: | Nikolay Atanasov, Kostas Daniilidis, Mabel M. Zhang |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
FOS: Computer and information sciences
0209 industrial biotechnology business.industry Computer science Monte Carlo tree search Cognitive neuroscience of visual object recognition Pattern recognition 02 engineering and technology Workspace 010501 environmental sciences Object (computer science) Robot end effector 01 natural sciences law.invention Computer Science - Robotics 020901 industrial engineering & automation law Robot Computer vision Artificial intelligence Physics engine business Focus (optics) Robotics (cs.RO) 0105 earth and related environmental sciences |
| Zdroj: | IROS |
| Popis: | This paper considers the problem of active object recognition using touch only. The focus is on adaptively selecting a sequence of wrist poses that achieves accurate recognition by enclosure grasps. It seeks to minimize the number of touches and maximize recognition confidence. The actions are formulated as wrist poses relative to each other, making the algorithm independent of absolute workspace coordinates. The optimal sequence is approximated by Monte Carlo tree search. We demonstrate results in a physics engine and on a real robot. In the physics engine, most object instances were recognized in at most 16 grasps. On a real robot, our method recognized objects in 2--9 grasps and outperformed a greedy baseline. Accepted to International Conference on Intelligent Robots and Systems (IROS) 2017 |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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