Optimization‐based Full Body Control for the DARPA Robotics Challenge

Abstract

We describe our full body humanoid control approach developed for the simulation phase of the DARPA Robotics Challenge (DRC), as well as the modifications made for the DARPA Robotics Challenge Trials. We worked with the Boston Dynamics Atlas robot. Our approach was initially targeted at walking, and it consisted of two levels of optimization: a high‐level trajectory optimizer that reasons about center of mass and swing foot trajectories, and a low‐level controller that tracks those trajectories by solving floating base full body inverse dynamics using quadratic programming. This controller is capable of walking on rough terrain, and it also achieves long footsteps, fast walking speeds, and heel‐strike and toe‐off in simulation. During development of these and other whole body tasks on the physical robot, we introduced an additional optimization component in the low‐level controller, namely an inverse kinematics controller. Modeling and torque measurement errors and hardware features of the Atlas robot led us to this three‐part approach, which was applied to three tasks in the DRC Trials in December 2013.