The members of the Dynamic Robotics and Artificial intelligence Laboratory (DRAIL) seek to achieve agile locomotion for legged robotic systems. Through creating robots that move swiftly and can easily handle impacts and kinetic energy transfer, we hope to push the limits of bipedal robots to make them useful in the real world. Examples include running, skipping, hopping, and walking up/down stairs. Many of these tasks are difficult due to hybrid, nonlinear dynamics caused by contacts, and ambiguous reward specification. To achieve these goals, our research aims to combine the first principles of legged locomotion with learned control systems.
Our focus is on both the control hierarchy as a whole as well as the individual low level components. For each piece, we want to identify the correct structure based on our knowledge of dynamics and apply learning to solve individual difficult problems. Ultimately, we hope to create an explainable and predictable framework that allows for consistently high performance locomotion on physical hardware in real world environments.