Method and device for controlling a robot
AssigneeRobert Bosch GmbH
InventorsHong Linh Thai, Jan Peters, Michael Herman
Life of this patent
Estimating the term…
Abstract
A computer-implemented method for applying control to a robot, and apparatus therefor. A parametric model of an environment, in particular a deep neural network, is trained in accordance with a method for training the parametric model of the environment. The model is trained depending on a controlled system. A strategy is learned in accordance with a method for model-based learning of the strategy. Control is applied to the robot depending on the parametric model and on the strategy.
Claims13
1-12. (canceled)
A computer-implemented method for training a parametric model of an environment, the model being a deep neural network, the method comprising the following steps:
- determining by the model, a new model state depending on a model state, on an action, and on at least one parameter of the model;
- wherein the parameter of the model is determined depending on a reward, the reward being determined depending on an expert trajectory and on a model trajectory determined in accordance with a strategy depending on the model state, and the expert trajectory is determined depending on a demonstration.
A computer-implemented method for applying control to a robot, the method comprising:
- training a parametric model of an environment, the model being a deep neural network, the training including:
- determining by the model, a new model state depending on a model state, on an action, and on at least one parameter of the model;
- wherein the parameter of the model is determined depending on a reward, the reward being determined depending on an expert trajectory and on a model trajectory determined in accordance with a strategy depending on the model state, and the expert trajectory is determined depending on a demonstration;
- learning the strategy for applying control to the robot;
- and
- applying control to the robot depending on the parametric model and on the strategy.
A non-transitory computer-readable memory on which is stored a computer program, including computer-readable instructions for training a parametric model of an environment, the model being a deep neural network, the instructions, when executed by a computer, causing the computer to perform the following steps:
- determining by the model, a new model state depending on a model state, on an action, and on at least one parameter of the model;
- wherein the parameter of the model is determined depending on a reward, the reward being determined depending on an expert trajectory and on a model trajectory determined in accordance with a strategy depending on the model state, and the expert trajectory is determined depending on a demonstration.
An apparatus for applying control to a robot, the apparatus configured to:
- train a parametric model of an environment, the model being a deep neural network, the training including:
- determining by the model, a new model state depending on a model state, on an action, and on at least one parameter of the model;
- wherein the parameter of the model is determined depending on a reward, the reward being determined depending on an expert trajectory and on a model trajectory determined in accordance with a strategy depending on the model state, and the expert trajectory is determined depending on a demonstration;
- learn the strategy for applying control to the robot;
- and
- apply control to the robot depending on the parametric model and on the strategy.
Dependencies are inferred from the claim text.