| CPC G06N 20/00 (2019.01) [G05B 19/4155 (2013.01); G06F 16/285 (2019.01); G06N 3/08 (2013.01); G06N 3/098 (2023.01); G05B 2219/42018 (2013.01)] | 20 Claims |
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1. A method, comprising:
detecting at a processing resource and via a sensor of a robot, an object in a path of the robot while the robot is performing a task in an environment;
classifying at the processing resource the object as an anomaly or a non-anomaly and the environment as anomalous or non-anomalous using a first machine learning model;
responsive to the processing resource's classification of the object as an anomaly or the environment as anomalous using the first machine learning model, utilizing a second machine learning model to confirm whether or not the object or the environment is anomalous and what type of anomaly the object or the environment is;
proceeding with the task responsive to the processing resource's classification and confirmation of the object as a non-anomaly and the environment as non-anomalous;
determining a plurality of potential resolutions to the anomaly or the anomalous environment responsive to the processing resource's classification and confirmation of the object as an anomaly or the environment as anomalous;
selecting one of the plurality of potential resolutions utilizing a third machine learning model;
resolving the anomaly or the anomalous environment utilizing the selected potential resolution before proceeding with the task;
detecting a new object or an addition to the environment;
classifying the new object or the addition to the environment as an anomaly until a decision is made otherwise based on receipt of user instructions;
collecting data associated with the new object or the addition to the environment; and
training the first machine learning model based on the collected data and the user instructions.
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