Training and/or using a machine learning model for locomotion control of a robot, where the model is decoupled. In many implementations, the model is decoupled into an open loop component and a feedback component, where a user can provide a desired reference trajectory (e.g., a symmetric sine curve) as input for the open loop component. In additional and/or alternative implementations, the model is decoupled into a pattern generator component and a feedback component, where a user can provide controlled parameter(s) as input for the pattern generator component to generate pattern generator phase data (e.g., an asymmetric sine curve). The neural network model can be used to generate robot control parameters.
SYSTEMS AND METHODS FOR LEARNING AGILE LOCOMOTION FOR MULTIPED ROBOTS
SYSTEME UND VERFAHREN ZUM LERNEN VON AGILER FORTBEWEGUNG FÜR MEHRBEINIGE ROBOTER
SYSTÈMES ET PROCÉDÉS D'APPRENTISSAGE DE LOCOMOTION AGILE POUR ROBOTS À PIEDS MULTIPLES
2021-01-27
Patent
Electronic Resource
English
SYSTEMS AND METHODS FOR LEARNING AGILE LOCOMOTION FOR MULTIPED ROBOTS
European Patent Office | 2021
|SYSTEMS AND METHODS FOR LEARNING AGILE LOCOMOTION FOR MULTIPED ROBOTS
European Patent Office | 2019
|SYSTEMS AND METHODS FOR LEARNING AGILE LOCOMOTION FOR MULTIPED ROBOTS
European Patent Office | 2021
|