ContactNet: Online Multi-Contact Planning for Acyclic Legged Robot Locomotion

In legged logomotion, online trajectory optimization techniques generally depend on heuristic-based contact planners in order to have low computation times and achieve high replanning frequencies. In this work, we propose ContactNet, a fast acyclic contact planner based on a multi-output regression...

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Bibliographic Details
Published inarXiv.org
Main Authors Bratta, Angelo, Meduri, Avadesh, Focchi, Michele, Righetti, Ludovic, Semini, Claudio
Format Paper Journal Article
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 02.05.2024
Subjects
Computation
Computer Science - Robotics
Locomotion
Neural networks
Predictive control
Robot dynamics
Trajectory optimization
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Summary:In legged logomotion, online trajectory optimization techniques generally depend on heuristic-based contact planners in order to have low computation times and achieve high replanning frequencies. In this work, we propose ContactNet, a fast acyclic contact planner based on a multi-output regression neural network. ContactNet ranks discretized stepping regions, allowing to quickly choose the best feasible solution, even in complex environments. The low computation time, in the order of 1 ms, makes possible the execution of the contact planner concurrently with a trajectory optimizer in a Model Predictive Control (MPC) fashion. We demonstrate the effectiveness of the approach in simulation in different complex scenarios with the quadruped robot Solo12.
ISSN:2331-8422
DOI:10.48550/arxiv.2209.15566