Synthesis of Robot Hand Skills Powered by Crowdsourced Learning

Document Type

Conference Proceeding

Publication Date

5-24-2019

Abstract

Crowdsourcing has shown great potentials in artificial intelligence. Continuous learning from a large group of mentors breaks the limit of learning from one or a few mentors in individual cases, and has achieved success in image recognition, translation and many other cyber applications. We bring the power of crowdsourcing to robot physical intelligence and introduce a learning method that allows robots to synthesize new physical skills using knowledge acquired from crowd-sourced human mentors. In addition, we provide a solution to sustainably manage a continuously growing massive knowledge library. The method is validated using a virtual reality interface and a simulated test of robot in-hand manipulation. The work has the potential of robotizing many demanding tasks that are currently hard to automate due to the demanding requirement of hand skills. The effectiveness of crowdsourced learning is evaluated by studying the success rate of new skill synthesis and the performance of the synthesized skills.

Identifier

85067116690 (Scopus)

ISBN

[9781538669594]

Publication Title

Proceedings 2019 IEEE International Conference on Mechatronics Icm 2019

External Full Text Location

https://doi.org/10.1109/ICMECH.2019.8722953

First Page

211

Last Page

216

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