Abstract: Systems and methods for training a neural network to predict states of a robotics device are disclosed. Robotics data is received for a robotics device, including indications of a set of components, a digital simulation of the robotics device, and measurement data received from a sensor associated with the robotics device. The set of components includes an actuator and a structural element. A training dataset is generated using the received robotics data. Generating the training dataset includes comparing the measurement data with simulated measurement data based on the digital simulation. A neural network is trained using the generated training dataset to modify the digital simulation of the robotics device to predict a state of the robotics device, such as a position, motion, electrical quantity, or other. When trained, the neural network is applied to predict states of the robotics device or a different robotics device.

Abstract: One embodiment of the present invention sets forth a technique for performing shape and appearance reconstruction. The technique includes generating a first set of renderings associated with an object based on a set of parameters that represent a reconstruction of the object in a first target image. The technique also includes producing, via a neural network, a first set of corrections associated with at least a portion of the set of parameters based on the first target image and the first set of renderings. The technique further includes generating an updated reconstruction of the object based on the first set of corrections.