Tiny Robot Learning: Challenges and Directions for Machine Learning in Resource-Constrained Robots
It addresses the problem of integrating ML into low-cost autonomous robots for robotics and embedded systems, but is incremental as it primarily reviews and outlines challenges without presenting new results.
The paper surveys tiny robot learning, which involves deploying machine learning on resource-constrained robots, highlighting challenges like SWAP constraints and hardware limitations, and proposes future directions for holistic ML system design.
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.