Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating synthetic data to be used in training machine learning models in an auto-scalable manner. In one aspect, an auto-scalable synthetic data generation system maintains a plurality of synthetic data generator replicas that are each configured to generate synthetic training examples; maintains a plurality of machine learning training workers that are each configured to obtain synthetic training examples and to use the synthetic training examples to concurrently perform operations required to update the machine learning model; determines, by an autoscaler of the synthetic data generation system, that a number of synthetic data generator replicas is insufficient to service a current demand level of the plurality of machine learning training workers; and in response, deploys, by the autoscaler, one or more additional synthetic data generator replicas in the synthetic data generation system.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for planning robotic movements to perform a given task while satisfying object pose estimation accuracy requirements. One of the methods includes generating a plurality of candidate measurement configurations for measuring an object to be manipulated by a robot; determining respective measurement accuracies for the plurality of candidate measurement configurations; determining a measurement accuracy landscape for the object including defining a high measurement accuracy region based on the respective measurement accuracies for the plurality of candidate measurement configurations; and generating a motion plan for manipulating the object in the robotic process that moves the robot, a sensor, or both, through the high measurement accuracy region when performing pose estimation for the object.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for planning robotic movements to perform a given task while satisfying object pose estimation accuracy requirements. One of the methods includes generating a plurality of candidate measurement configurations for measuring an object to be manipulated by a robot; determining respective measurement accuracies for the plurality of candidate measurement configurations; determining a measurement accuracy landscape for the object including defining a high measurement accuracy region based on the respective measurement accuracies for the plurality of candidate measurement configurations; and generating a motion plan for manipulating the object in the robotic process that moves the robot, a sensor, or both, through the high measurement accuracy region when performing pose estimation for the object.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing image data. One of the method includes receiving an input image from a source domain, the input image showing an object to be manipulated by a robot in a robotic process; processing the input image to generate an intermediate representation of the input image, comprising: generating a gradient orientation representation and a gradient magnitude representation of the input image; and generating the intermediate representation of the input image from the gradient orientation representation and the gradient magnitude representation; processing the intermediate representation of the input image using a neural network trained to make predictions about objects in images to generate a network output that represents a prediction about physical characteristics of the object in the input image.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a feature extraction neural network to generate domain-invariant feature representations from domain-varying input images. In one aspect, the method includes obtaining a training dataset comprising a first set of target domain images and a second set of real domain images that each have pixel-wise level alignment with a corresponding target domain image, and training the feature extraction neural network on the training dataset based on optimizing an objective function that includes a term that depends on a similarity between respective feature representations generated by the network for a pair of target and source domain images.