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: An electronic device is configured to performing a three-dimensional (3D) scan of an interior space. In some cases, the electronic device acquires information and depth measurements relative to the electronic device. The electronic device acquires voxels in a 3D grid that is generated from the 3D scan. The voxels represent portions of the volume of the interior space. The electronic device determines a trajectory and poses of the electronic device concurrently with performing the 3D scan of the interior space. The electronic device labels voxels representing objects in the interior space based on the trajectory and the poses. In some cases, the electronic device uses queries to perform spatial reasoning at an object level of granularity, positions, overlays, or blends virtual objects into an augmented reality representation of the interior space or modifies positions or orientations of the objects by applying a transformation to corresponding connected components.

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.

Abstract: An electronic device is configured to performing a three-dimensional (3D) scan of an interior space. In some cases, the electronic device acquires information and depth measurements relative to the electronic device. The electronic device acquires voxels in a 3D grid that is generated from the 3D scan. The voxels represent portions of the volume of the interior space. The electronic device determines a trajectory and poses of the electronic device concurrently with performing the 3D scan of the interior space. The electronic device labels voxels representing objects in the interior space based on the trajectory and the poses. In some cases, the electronic device uses queries to perform spatial reasoning at an object level of granularity, positions, overlays, or blends virtual objects into an augmented reality representation of the interior space or modifies positions or orientations of the objects by applying a transformation to corresponding connected components.