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. C 2014 Wiley Periodicals, Inc.
One popular approach to controlling humanoid robots is through inverse kinematics (IK) with stiff joint position tracking. On the other hand, inverse dynamics (ID) based approaches have gained increasing acceptance by providing compliant motions and robustness to external perturbations. However, the performance of such methods is heavily dependent on high quality dynamic models, which are often very difficult to produce for a physical robot. IK approaches only require kinematic models, which are much easier to generate in practice. In this paper, we supplement our previous work with ID-based controllers by adding IK, which helps compensate for modeling errors. The proposed full body controller is applied to three tasks in the DARPA Robotics Challenge (DRC) Trials in Dec. 2013.
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