Abstract: In various examples, systems and methods are disclosed relating to training machine learning models using human demonstration of segments of a task, where other segments of the task are performed by a planning method, such as a Task and Motion Planning (TAMP) system. A method may include segmenting a task to be performed by a robot into segments, determining a first set of instructions of a plurality of sets of instructions for operating the robot to perform a first objective of a first segment, determining that the plurality of sets of instructions is inadequate to perform a second objective of a second segment, receiving from a user device a second set of instructions for operating the robot for the second segment following an end of the first segment, and updating a machine learning model for controlling the robot using the second set of instructions for the second segment.

Abstract: One embodiment of a method for generating simulation data to train a machine learning model includes generating a plurality of simulation environments based on a user input, and for each simulation environment included in the plurality of simulation environments: generating a plurality of tasks for a robot to perform within the simulation environment, performing one or more operations to determine a plurality of robot trajectories for performing the plurality of tasks, and generating simulation data for training a machine learning model by performing one or more operations to simulate the robot moving within the simulation environment according to the plurality of trajectories.

Abstract: Apparatuses, systems, and techniques to perform collision-free motion generation (e.g., to operate a real-world or virtual robot). In at least one embodiment, at least a portion of the collision-free motion generation is performed in parallel.

Abstract: An image processing system and method corrects the perceptual quality of an image by adjusting the image colors. “Color” in this sense is to be understood as encompassing three-dimensional parameterizations of color, incorporating both intensity and hue. These measurements are made using creative computations developed and adapted from the RETINEX™ theory (Land, 1977), where the RETINEX implementation adjusts any pixel in any direction in color space and further to automatically determine the best direction for it to be adjusted to increase the perceptual visibility of the algorithm with a minimal amount of change to the original image values.

Abstract: An image processing system and method corrects the perceptual quality of an image by adjusting the image colors. “Color” in this sense is to be understood as encompassing three-dimensional parameterizations of color, incorporating both intensity and hue. These measurements are made using creative computations developed and adapted from the RETINEX™ theory (Land, 1977), where the RETINEX implementation adjusts any pixel in any direction in color space and further to automatically determine the best direction for it to be adjusted to increase the perceptual visibility of the algorithm with a minimal amount of change to the original image values.