Legged locomotion is a high-dimensional, highly dynamic, self-stable system that is comprised of passive elements, such as springs, and active control from sensors and computing. Animals, our best example of this dynamical system, are able to negotiate terrain that varies widely in height as well as firmness, with excellent energy economy. Robots cannot yet approach animal performance, and we contend that this lack of ability by robots is a result of lack of scientific understanding of fundamental principles of legged locomotion rather than any technological limitation.
We seek to answer these fundamental questions of how legged locomotion works, and to demonstrate discoveries by building robots and implementing principled controllers. We have found that simple controllers of the swing leg during flight can replicate observed behavior from animals, including the prioritization of injury avoidance over a steady gait in uneven terrain. Further, we have shown that a simple stance-phase force control method can explain observed biological features such as apparent leg stiffness changes or energy insertion on dissipative ground. The combination of these straightforward controllers allows a simple model to handle surprisingly variable terrain with no terrain knowledge. We currently are implementing these controllers on ATRIAS, our bipedal robot.