Name
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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ICMECH.2019.8722953 | 2019 IEEE International Conference on Mechatronics (ICM) |
Keywords | Field | DocType |
Libraries,Aerospace electronics,Trajectory,Crowdsourcing,Task analysis,Robot learning | Robot hand,Control engineering,Engineering | Conference |
Volume | ISBN | Citations |
1 | 978-1-5386-6959-4 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Leidi Zhao | 1 | 0 | 1.01 |
| Raheem Lawhorn | 2 | 0 | 0.68 |
| Cong Wang | 3 | 4463 | 204.50 |
| Lu, G. | 4 | 5 | 3.89 |
| Bo Ouyang | 5 | 1 | 2.04 |