3 accepted papers
We want robots to complete assigned tasks even when unexpected task pressures arise, either from the robot or the environment. This paper presents a method of both learning sources of task failure in situ and rapidly planning new motions on-the-fly to accommodate them. This “risk-adaptive” approach…
Robots encounter many risks that threaten the success of practical locomotion tasks. Legs break, electrical components overheat, and feet can unexpectedly slip. When all risks cannot be completely avoided, how does a robot decide its best action? We present a method for planning robot motions by rea…
We investigate the task-optimality of legged limit cycles and present numerical evidence supporting a simple general locomotion-planning template. Limit cycles have been foundational to the control and analysis of legged systems, but as robots move toward completing real-world tasks, are limit cycle…