The Robotics Institute

Carnegie Mellon Robotics Institute

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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 “certificates” that ensure the output of an abstract high-level planner can be realized by deeper 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 rough terrains.

Keywords

legged locomotion, motion planning, machine learning

Notes

Sponsor: DARPA

Associated Center(s) / Consortia: Center for the Foundations of Robotics

Associated Lab(s) / Group(s): Planning and Autonomy Lab

Associated Project(s): Learning Locomotion

Text Reference

Matthew Zucker, J. Andrew (Drew) Bagnell, Chris Atkeson, and James Kuffner, "An Optimization Approach to Rough Terrain Locomotion," IEEE Conference on Robotics and Automation, May, 2010.

BibTeX Reference

@inproceedings{Zucker_2010_6570,
   author = "Matthew Zucker and J. Andrew (Drew) Bagnell and Chris Atkeson and James Kuffner",
   title = "An Optimization Approach to Rough Terrain Locomotion",
   booktitle = "IEEE Conference on Robotics and Automation",
   month = "May",
   year = "2010",
}

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