Event Location: NSH 3305

Abstract: This thesis proposal presents an online approach that is based on hierarchical optimization for controlling humanoid robots. While our primary focus is on developing a fast and robust walking system that is able to follow desired footsteps, full body manipulation capability is also achieved.

The proposed hierarchical system consists of three levels:

A high level trajectory optimizer that generates nominal center of mass and swing foot trajectories, together with useful information such as a local value function approximation and a linear policy along the nominal trajectories.

A middle level receding-horizon controller that tracks the nominal plan and handles large disturbances obeying center of pressure constraints.

– A low level controller that computes joint level commands by solving full body inverse dynamics and kinematics using quadratic programming.

The current implementation is capable of walking on rough terrain, and achieves close to human walking speed and stride length in simulation. It has also been successfully applied to the Atlas robot, built by Boston Dynamics for the DARPA Robotics Challenge, in which static walking over rough terrain and full body manipulation have been demonstrated. Future work focuses on implementing a fast robust walking algorithm on the real Atlas robot using the full hierarchy.

Committee:Christopher G. Atkeson, Chair
Hartmut Geyer
Koushil Sreenath
Jerry Pratt, Institute for Human and Machine Cognition