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.
训练和/或使用机器学习模型用于机器人的运动控制,其中将模型解耦。在许多实现方式中,模型被解耦为开环分量和反馈分量,其中用户可以提供期望的参考轨迹(例如,对称正弦曲线)作为开环分量的输入。在附加的和/或可替代的实现方式中,模型被解耦为模式生成器分量和反馈分量,其中用户可以提供受控参数(或多个)作为模式生成器分量的输入,以生成模式生成器相位数据(例如,非对称正弦曲线)。神经网络模型可用于生成机器人控制参数。
SYSTEMS AND METHODS FOR LEARNING AGILE LOCOMOTION FOR MULTIPED ROBOTS
学习多足机器人的敏捷运动的系统和方法
2021-01-12
Patent
Electronic Resource
Chinese
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
|SYSTEMS AND METHODS FOR LEARNING AGILE LOCOMOTION FOR MULTIPED ROBOTS
European Patent Office | 2021
|