タスク関連表現学習によるネットワーク化ロボット知覚
Task-relevant Representation Learning for Networked Robotic Perception
ロボットの高次タスクに必要な情報だけを残すよう知覚モデルと共設計した圧縮表現を学習し、従来法より最大11倍の圧縮を実現した論文。
著者: Manabu Nakanoya, Sandeep Chinchali, Alexandros Anemogiannis, Akul Datta, Sachin Katti, Marco Pavone
分類: cs.RO, cs.CV, cs.IT, cs.NI, cs.SY, eess.SY, math.IT
原文アブストラクト
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.
