Optimization and Learning for Rough Terrain Legged Locomotion¶
Authors: Matthew Zucker, Nathan Ratliff, Martin Stolle, Joel Chestnutt, J. Andrew Bagnell, Christopher G. Atkeson, James Kuffner
Published: 2011 (Journal Paper)
Source: International Journal of Robotics Research
DOI: 10.1177/0278364910392608
Summary¶
Proposes a hierarchical optimization framework for quadruped locomotion over rough terrain, combining anytime footstep planning with dynamic body motion optimization. Uses inverse optimal control to learn cost functions from demonstrations and real-time re-planning for robustness, demonstrated on the LittleDog robot.
Abstract¶
This paper presents a novel approach to legged locomotion over rough terrain rooted in optimization. The approach relies on a hierarchy of fast, anytime algorithms to plan a set of footholds and 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 lower layers of the hierarchy. The burden of careful engineering of cost functions is mitigated by a simple inverse optimal control technique, and robustness is achieved by real-time re-planning of the full trajectory, augmented by reflexes and feedback control. We demonstrate the approach guiding the LittleDog quadruped robot over various rough terrains.
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Tags¶
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Legged locomotion
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Rough terrain
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Footstep planning
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Trajectory optimization
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Inverse optimal control
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Quadruped
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Anytime planning