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Motion Planning, Perception and Learning Algorithms towards more Adaptive Robots

Wednesday, November 15th, 2017, 16:10

Schreiber 309

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Motion Planning, Perception and Learning Algorithms towards more Adaptive Robots

Kostas Bekris, Rutgers University

Abstract:

Robots have thrived in highly-structured, accurately known and safely enclosed industrial settings, where they achieve high-speed, accuracy and consistency.  Modern robotics aspires to deal with unstructured environments and arbitrary terrains so as to impact domains, such as logistics, service and field robotics, where adaptability is a critical objective. These altered priorities motivate both new algorithms with desirable properties in the context of these applications as well as the development of new robots, such as safe, compliant mechanisms.

This talk will first focus on the manipulation of objects that appear in novel configurations and clutter using robotic arms. Perception and planning aspects will be covered in identifying and rearranging objects, as well as coordinating the motion of multiple arms. The focus will then transition to a compliant locomotion robot inspired by the principle of tensegrity.  This is a domain where machine learning can critically impact applications given the difficulty in acquiring accurate models. This talk will cover a promising direction in controlling such a novel system and conclude with a vision of integrating learning and planning to bridge the gap between skills and objectives of modern robotics.

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