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MACHINE LEARNING METHODS AND APPARATUS RELATED TO PREDICTING MOTIONS OF OBJECTS IN A ROBOT'S ENVIRONMENT BASED ON IMAGES CAPTURING THE OBJECTS AND BASED ON PARAMETERS FOR FUTURE ROBOT MOVEMENT IN THE ENVIRONMENT
MACHINE LEARNING METHODS AND APPARATUS RELATED TO PREDICTING MOTIONS OF OBJECTS IN A ROBOT'S ENVIRONMENT BASED ON IMAGES CAPTURING THE OBJECTS AND BASED ON PARAMETERS FOR FUTURE ROBOT MOVEMENT IN THE ENVIRONMENT
Some embodiments of the present disclosure generally relate to a dip machine learning method and apparatus for predicting motion (s) (if any) to occur with respect to object (s) in an environment of a robot in response to a particular motion of the robot . Some implementations use a deep neural network model to predict at least one transformation (if any) of an image of the environment of the robot that occurs as a result of implementing at least some of the robot's specific movements in the environment / RTI The trained deep neural network model can predict a transformation based on an input that includes an image and a group of robot motion parameters that define a portion of a particular motion.
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