TY - GEN
T1 - Synthesis of Robot Hand Skills Powered by Crowdsourced Learning
AU - Zhao, Leidi
AU - Lawhorn, Raheem
AU - Wang, Cong
AU - Lu, Lu
AU - Ouyang, Bo
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/5/24
Y1 - 2019/5/24
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85067116690&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85067116690&partnerID=8YFLogxK
U2 - 10.1109/ICMECH.2019.8722953
DO - 10.1109/ICMECH.2019.8722953
M3 - Conference contribution
AN - SCOPUS:85067116690
T3 - Proceedings - 2019 IEEE International Conference on Mechatronics, ICM 2019
SP - 211
EP - 216
BT - Proceedings - 2019 IEEE International Conference on Mechatronics, ICM 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 IEEE International Conference on Mechatronics, ICM 2019
Y2 - 18 March 2019 through 20 March 2019
ER -