Transformation for covariate shift of grasp neural networks

A method for determining grasp points associated with an autonomous machine that defines an end effector configured to grasp objects, the method comprising:

• training a neural network on images of a plurality of objects;
• sending an image of a new object to the neural network, the new object not one of the plurality of objects on which the neural network is trained;
• based on the image and the end effector, the neural network generating an output that identifies grasp locations on the object, the output defining a covariate shift;
• and
• transforming the output so as to eliminate the covariate shift.

An autonomous system comprising:

• an autonomous machine defining an end effector configured to grasp an object within a physical environment, each of the object and the physical environment defining a plurality of locations;
• a sensor configured to capture an image of an object within the physical environment, the image defining a plurality of pixels representative of the plurality of locations of the object and the physical environment;
• one or more processors;
• and
• a memory storing instructions that, when executed by the one or more processors, cause the autonomous system to:
• based on the image, generate an output that defines respective grasp scores associated with the plurality of pixels, the grasp scores indicating a respective grasp quality at the locations of the object represented by the respective pixel;
• transform the output;
• based on transforming the output, select a grasp location of the plurality of locations of the object;
• and
• send an instruction to the autonomous machine to grasp the object at the grasp location,
• wherein the end effector is further configured to, based on the instruction, grasp the object at the grasp location.