Evaluating robot learning
AssigneeX Development LLC
InventorsNareshkumar Rajkumar, Patrick Leger
Life of this patent
Estimating the term…
Abstract
Methods, systems, and apparatus, including computer programs encoded on computer storage media for evaluating robot learning. In some implementations, one or more computers receive object classification examples from a plurality of robots. Each object classification example includes (i) an embedding that a robot generated using a machine learning model, and (ii) an object classification corresponding to the embedding. The object classification examples are evaluated based on a similarity of the received embeddings with respect to other embeddings. A subset of the object classification examples is selected based on the evaluation of the quality of the embeddings. The subset of the object classification examples is distributed to the robots in the plurality of robots.
Claims20
A method performed by one or more computers, the method comprising:
- receiving, by the one or more computers, object classification examples from a plurality of robots, wherein each object classification example includes (i) an embedding that a robot generated using a machine learning model, and (ii) an object classification corresponding to the embedding;
- evaluating, by the one or more computers, quality of the object classification examples based on a similarity of the embeddings of the received object classification examples with other embeddings;
- selecting, by the one or more computers, a subset of the object classification examples based on the evaluation of the quality of the object classification examples using the embeddings of the object classification examples;
- and
- distributing, by the one or more computers, the subset of the object classification examples to the robots in the plurality of robots.
A system comprising:
- one or more computers and one or more storage devices storing instructions that are operable, when executed by the one or more computers, to cause the one or more computers to perform operations comprising:
- receiving, by the one or more computers, object classification examples from a plurality of robots, wherein each object classification example includes (i) an embedding that a robot generated using a machine learning model, and (ii) an object classification corresponding to the embedding;
- evaluating, by the one or more computers, quality of the object classification examples based on a similarity of the embeddings of the received object classification examples with other embeddings;
- selecting, by the one or more computers, a subset of the object classification examples based on the evaluation of the quality of the object classification examples using the embeddings of the object classification examples;
- and
- distributing, by the one or more computers, the subset of the object classification examples to the robots in the plurality of robots.
One or more non-transitory computer-readable media storing software comprising instructions executable by one or more computers which, upon such execution, cause the one or more computers to perform operations comprising:
- receiving, by the one or more computers, object classification examples from a plurality of robots, wherein each object classification example includes (i) an embedding that a robot generated using a machine learning model, and (ii) an object classification corresponding to the embedding;
- evaluating, by the one or more computers, quality of the object classification examples based on a similarity of the embeddings of the received object classification examples with other embeddings;
- selecting, by the one or more computers, a subset of the object classification examples based on the evaluation of the quality of the object classification examples using the embeddings of the object classification examples;
- and
- distributing, by the one or more computers, the subset of the object classification examples to the robots in the plurality of robots.
Dependencies are inferred from the claim text.