X Development LLC

Google Inc.

Devesh Yamparala

Nicolas Hudson

Umashankar Nagarajan

Methods, apparatus, and computer-readable media for determining and utilizing human corrections to robot actions. In some implementations, in response to determining a human correction of a robot action, a correction instance is generated that includes sensor data, captured by one or more sensors of the robot, that is relevant to the corrected action. The correction instance can further include determined incorrect parameter(s) utilized in performing the robot action and/or correction information that is based on the human correction. The correction instance can be utilized to generate training example(s) for training one or model(s), such as neural network model(s), corresponding to those used in determining the incorrect parameter(s).

Determining and utilizing corrections to robot actions

Methods, apparatus, and computer-readable media for determining and utilizing human corrections to robot actions. In some implementations, in response to determining a human correction of a robot action, a correction instance is generated that includes sensor data, captured by one or more sensors of the robot, that is relevant to the corrected action. The correction instance can further include determined incorrect parameter(s) utilized in performing the robot action and/or correction information that is based on the human correction. The correction instance can be utilized to generate training example(s) for training one or model(s), such as neural network model(s), corresponding to those used in determining the incorrect parameter(s). In various implementations, the training is based on correction instances from multiple robots. After a revised version of a model is generated, the revised version can thereafter be utilized by one or more of the multiple robots.

Methods, systems, and apparatus, including computer-readable media, for robot grasp learning. In some implementations, grasp data describing grasp attempts by robots is received. A set of the grasp attempts that represent unsuccessful grasp attempts is identified. Based on the set of grasp attempts representing unsuccessful grasp attempts, a grasp model based on sensor data for the unsuccessful grasp attempts. After training the grasp model, a performance level of the trained grasp model is verified based on one or more simulations of grasp attempts. In response to verifying the performance level of the trained grasp model, the trained grasp model is provided to one or more robots.

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Mountain View, CA, United States of America

Devesh Yamparala