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Dynamics Consensus between Centroidal and Whole-Body Models for Locomotion of Legged Robots

Rohan Budhiraja 1 Justin Carpentier 1, 2 Nicolas Mansard 1 
1 LAAS-GEPETTO - Équipe Mouvement des Systèmes Anthropomorphes
LAAS - Laboratoire d'analyse et d'architecture des systèmes
2 WILLOW - Models of visual object recognition and scene understanding
DI-ENS - Département d'informatique - ENS Paris, Inria de Paris
Abstract : It is nowadays well-established that locomotion can be written as a large and complex optimal control problem. Yet, current knowledge in numerical solver fails to directly solve it. A common approach is to cut the dimensionality by relying on reduced models (inverted pendulum, capture points, centroidal). However it is difficult both to account for whole-body constraints at the reduced level and also to define what is an acceptable trade-off at the whole-body level between tracking the reduced solution or searching for a new one. The main contribution of this paper is to introduce a rigorous mathematical framework based on the Alternating Direction Method of Multipliers, to enforce the consensus between the centroidal state dynamics at reduced and whole-body level. We propose an exact splitting of the whole-body optimal control problem between the centroidal dynamics (under-actuation) and the manipulator dynamics (full actuation), corresponding to a rearrangement of the equations already stated in previous works. We then describe with details how alternating descent is a good solution to implement an effective locomotion solver. We validate this approach in simulation with walking experiments on the HRP-2 robot.
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Submitted on : Sunday, September 16, 2018 - 3:36:34 AM
Last modification on : Wednesday, June 8, 2022 - 12:50:06 PM
Long-term archiving on: : Monday, December 17, 2018 - 12:23:27 PM


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  • HAL Id : hal-01875031, version 1


Rohan Budhiraja, Justin Carpentier, Nicolas Mansard. Dynamics Consensus between Centroidal and Whole-Body Models for Locomotion of Legged Robots. 2018. ⟨hal-01875031v1⟩



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