Learning to Jump from Pixels

One of the grand challenges in robotics is to construct legged systems that can successfully navigate novel and complex landscapes. Recent work has made impressive strides toward the blind traversal of a wide diversity of natural and man-made terrains [1, 2]. Blind walkers primarily rely on proprioception and robust control schemes to achieve sturdy locomotion in challenging conditions including snow, thick vegetation, and slippery mud. The downside of blindness is the inability to execute motions that anticipate the land surface in front of the robot. This is especially prohibitive on terrains with significant elevation discontinuities. For instance, crossing a wide gap requires the robot to jump, which cannot be initiated without knowing where and how wide the gap is. Without vision, even the most robust system would either step in the gap and fall or otherwise treat the gap as an obstacle and stop. This inability to plan results in conservative behavior that is unable to achieve the energy efficiency or the speed afforded by advanced hardware.