タイニーロボット学習：リソース制約ロボットにおける機械学習の課題と方向性
Tiny Robot Learning: Challenges and Directions for Machine Learning in Resource-Constrained Robots
低コスト・省リソースの小型自律ロボットに機械学習を搭載する「タイニーロボット学習」の課題を整理し、組み込み・ロボティクス・MLの融合領域における今後の研究方向を展望したサーベイ。
著者: Sabrina M. Neuman, Brian Plancher, Bardienus P. Duisterhof, Srivatsan Krishnan, Colby Banbury, Mark Mazumder, Shvetank Prakash, Jason Jabbour, Aleksandra Faust, Guido C. H. E. de Croon, Vijay Janapa Reddi
分類: cs.LG, cs.RO
原文アブストラクト
Machine learning (ML) has become a pervasive tool across computing systems. An emerging application that stress-tests the challenges of ML system design is tiny robot learning, the deployment of ML on resource-constrained low-cost autonomous robots. Tiny robot learning lies at the intersection of embedded systems, robotics, and ML, compounding the challenges of these domains. Tiny robot learning is subject to challenges from size, weight, area, and power (SWAP) constraints; sensor, actuator, and compute hardware limitations; end-to-end system tradeoffs; and a large diversity of possible deployment scenarios. Tiny robot learning requires ML models to be designed with these challenges in mind, providing a crucible that reveals the necessity of holistic ML system design and automated end-to-end design tools for agile development. This paper gives a brief survey of the tiny robot learning space, elaborates on key challenges, and proposes promising opportunities for future work in ML system design.
