Task-relevant Representation Learning for Networked Robotic Perception
This addresses the resource constraints in robots like drones and rovers by reducing compute and network usage for perception tasks.
The paper tackles the problem of inefficient sensory data transmission for networked robotic perception by learning task-relevant representations that compress data up to 11x more than competing methods, achieving high accuracy on tasks like Mars terrain classification and neural motion planning.
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.