Optimization and learning for rough terrain legged locomotion

Authors:
Matt Zucker;Nathan Ratliff;Martin Stolle;Joel Chestnutt;J Andrew Bagnell;Christopher G Atkeson;James Kuffner
Affiliations:
Department of Engineering, Swarthmore College, 500 CollegeAvenue, Swarthmore, PA 19081, USA;Intel Research, 4720 Forbes Avenue Suite 410, Pittsburgh,PA 15213, USA;Google, Inc., Brandschenkestrasse 110, 8002 Zürich,Switzerland;Digital Human Research Center, National Institute ofAdvanced, Industrial Science and Technology, 2-3-26, Aomi, Koto-ku, Tokyo135-0064, Japan;The Robotics Institute, Carnegie Mellon University,5000 Forbes Avenue, Pittsburgh, PA 15213, USA;The Robotics Institute, Carnegie Mellon University,5000 Forbes Avenue, Pittsburgh, PA 15213, USA;Google, Inc., 1600 Amphitheatre Parkway, Mountain View,CA 94043, USA
Venue:
International Journal of Robotics Research
Year:
2011

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Abstract

We present a novel approach to legged locomotion over rough terrain that is thoroughly rooted in optimization. This approach relies on a hierarchy of fast, anytime algorithms to plan a set of footholds, along with the dynamic body motions required to execute them. Components within the planning framework coordinate to exchange plans, cost-to-go estimates, and â聙聵certificatesâ聙聶 that ensure the output of an abstract high-level planner can be realized by lower layers of the hierarchy. The burden of careful engineering of cost functions to achieve desired performance is substantially mitigated by a simple inverse optimal control technique. Robustness is achieved by real-time re-planning of the full trajectory, augmented by reflexes and feedback control. We demonstrate the successful application of our approach in guiding the LittleDog quadruped robot over a variety of types of rough terrain. Other novel aspects of our past research efforts include a variety of pioneering inverse optimal control techniques as well as a system for planning using arbitrary pre-recorded robot behavior.