Co-Design of Communication and Machine Inference for Cloud Robotics

Today; even the most compute-and-power constrained robots can measure complex; high data-rate video and LIDAR sensory streams. Often; such robots; ranging from low-power drones to space and subterranean rovers; need to transmit high-bitrate sensory data to a remote compute server if they are uncertain or cannot scalably run complex perception or mapping tasks locally. However; today's representations for sensory data are mostly designed for human; not robotic; perception and thus often waste precious compute or wireless network resources to transmit unimportant parts of a scene that are unnecessary for a high-level robotic task. This paper presents an algorithm to learn task-relevant representations of sensory data that are co-designed with a pre-trained robotic perception model's ultimate objective. Our algorithm aggressively compresses robotic sensory data by up to 11x more than competing methods. Further; it achieves high accuracy and robust generalization on diverse tasks including Mars terrain classification with low-power deep learning accelerators; neural motion planning; and environmental timeseries classification.