Fast and Flexible Multilegged Locomotion Using Learned Centroidal Dynamics | |||||||||||||
TAESOO KWON | YOONSANG LEE | MICHIEL VAN DE PANNE | |||||||||||
❶ ABSTRACT | |||||||||||||
We present a flexible and efficient approach for generating multilegged
locomotion. Our model-predictive control (MPC) system efficiently generates
terrain-adaptive motions, as computed using a three-level planning approach.
This leverages two commonly-used simplified dynamics models, an inverted
pendulum on a cart model (IPC) and a centroidal dynamics model (CDM).
Taken together, these ensure efficient computation and physical fidelity
of the resulting motion. The final full-body motion is generated using a
novel momentum-mapped inverse kinematics solver and is responsive to
external pushes by using CDM forward dynamics. For additional efficiency
and robustness, we then learn a predictive model that then replaces two of
the intermediate steps. We demonstrate the rich capabilities of the method
by applying it to monopeds, bipeds, and quadrupeds, and showing that it
can generate a very broad range of motions at interactive rates, including
banked variable-terrain walking and running, hurdles, jumps, leaps, stepping
stones, monkey bars, implicit quadruped gait transitions, moon gravity, push-
responses, and more.
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❷ VIDEO | |||||||||||||
❸ DOWNLOAD | PAPER(pdf,4.6MB) | VIDEO(mp4,50MB) | |||||||||||
❹ CONTACT | |||||||||||||
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Taesoo Kwon
taesoo@hanyang.ac.kr
● Yoonsang Lee yoonsanglee@hanyang.ac.kr ● Michiel van de Panne van@cs.ubc.ca |
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❺ SOURCE CODE (online part only) | |||||||||||||
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link
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