  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.
