Robot Composite Learning and the Nunchaku Flipping Challenge
This addresses the challenge of enabling robots to perform extreme dynamic tasks for applications in human-robot coexistence, though it appears incremental as it builds on previous work.
The paper tackles the problem of robots acquiring advanced motor skills like dynamic maneuvers and complex contact control by introducing a composite learning scheme that integrates human definition, demonstration, and evaluation, achieving physical success in the nunchaku flipping challenge.
Advanced motor skills are essential for robots to physically coexist with humans. Much research on robot dynamics and control has achieved success on hyper robot motor capabilities, but mostly through heavily case-specific engineering. Meanwhile, in terms of robot acquiring skills in a ubiquitous manner, robot learning from human demonstration (LfD) has achieved great progress, but still has limitations handling dynamic skills and compound actions. In this paper, we present a composite learning scheme which goes beyond LfD and integrates robot learning from human definition, demonstration, and evaluation. The method tackles advanced motor skills that require dynamic time-critical maneuver, complex contact control, and handling partly soft partly rigid objects. We also introduce the "nunchaku flipping challenge", an extreme test that puts hard requirements to all these three aspects. Continued from our previous presentations, this paper introduces the latest update of the composite learning scheme and the physical success of the nunchaku flipping challenge.