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US 12,764,414 B2Granted
Training camera policy neural networks through self-prediction
AssigneeGDM Holding LLC
InventorsMatthew Koichi Grimes, Piotr Wojciech Mirowski, Joseph Varughese Modayil
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Abstract
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a camera policy neural network.
Claims24
A method for training a camera policy neural network that is used to control a position of a camera sensor in an environment being interacted with by a robot, the method comprising:
- obtaining data specifying one or more target sensors of the robot;
- obtaining a first observation comprising one or more images of the environment captured by the camera sensor while at a current position;
- processing a camera policy input comprising (i) the data specifying one or more target sensors of the robot and (ii) the first observation that comprises one or more images captured by the camera sensor using the camera policy neural network to generate a camera policy output that defines a camera control action for adjusting the position of the camera sensor;
- adjusting the current position of the camera sensor based on the camera control action;
- obtaining a second observation comprising one or more images of the environment captured by the camera sensor while at the adjusted position;
- generating, from the second observation, a respective prediction for each of the one or more target sensors that characterizes sensor readings generated by the target sensor;
- generating, for each target sensor, a respective reward for the camera policy neural network from an error between the respective prediction for the target sensor and a respective ground truth value for the target sensor;
- and
- training the camera policy neural network using the rewards for the one or more target sensors.
6 more elements
A system comprising:
- one or more computers;
- and
- one or more storage devices communicatively coupled to the one or more computers, wherein the one or more storage devices store instructions that, when executed by the one or more computers, cause the one or more computers to perform operations for training a camera policy neural network that is used to control a position of a camera sensor in an environment being interacted with by a robot, the operations comprising:
- obtaining data specifying one or more target sensors of the robot;
- obtaining a first observation comprising one or more images of the environment captured by the camera sensor while at a current position;
- processing a camera policy input comprising (i) the data specifying one or more target sensors of the robot and (ii) the first observation that comprises one or more images captured by the camera sensor using the camera policy neural network to generate a camera policy output that defines a camera control action for adjusting the position of the camera sensor;
- adjusting the current position of the camera sensor based on the camera control action;
- obtaining a second observation comprising one or more images of the environment captured by the camera sensor while at the adjusted position;
- generating, from the second observation, a respective prediction for each of the one or more target sensors that characterizes sensor readings generated by the target sensor;
- generating, for each target sensor, a respective reward for the camera policy neural network from an error between the respective prediction for the target sensor and a respective ground truth value for the target sensor;
- and
- training the camera policy neural network using the rewards for the one or more target sensors.
9 more elements
One or more non-transitory computer storage media storing instructions that when executed by one or more computers cause the one or more computers to perform operations for training a camera policy neural network that is used to control a position of a camera sensor in an environment being interacted with by a robot, the operations comprising:
- obtaining data specifying one or more target sensors of the robot;
- obtaining a first observation comprising one or more images of the environment captured by the camera sensor while at a current position;
- processing a camera policy input comprising (i) the data specifying one or more target sensors of the robot and (ii) the first observation that comprises one or more images captured by the camera sensor using the camera policy neural network to generate a camera policy output that defines a camera control action for adjusting the position of the camera sensor;
- adjusting the current position of the camera sensor based on the camera control action;
- obtaining a second observation comprising one or more images of the environment captured by the camera sensor while at the adjusted position;
- generating, from the second observation, a respective prediction for each of the one or more target sensors that characterizes sensor readings generated by the target sensor;
- generating, for each target sensor, a respective reward for the camera policy neural network from an error between the respective prediction for the target sensor and a respective ground truth value for the target sensor;
- and
- training the camera policy neural network using the rewards for the one or more target sensors.
6 more elements
Dependencies are inferred from the claim text.