Improving Robot Locomotion Through Learning Methods for Expensive Black-Box Systems.

摘要

The modular snake robots in Carnegie Mellons Biorobotics lab provide an intriguing platform for research: they have already been shown to excel at a variety of locomotive tasks and have incredible potential for navigating complex terrains, but much of that potential remains untapped. Unfortunately, many techniques commonly used in robotics prove inapplicable to these snake robots. This is because of the robots complex, multi-modal locomotion dynamics, which are difficult to model, and their small size and frequent impacts, which preclude addition of many standard sensors.