@conference {neuman2022tiny,
	title = {Tiny robot learning: challenges and directions for machine learning in resource-constrained robots},
	booktitle = {2022 IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS)},
	year = {2022},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {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.},
	url = {https://arxiv.org/pdf/2205.05748.pdf},
	author = {<b>Sabrina M. Neuman</b> and Brian Plancher and Duisterhof, Bardienus P. and Srivatsan Krishnan and Colby Banbury and Mark Mazumder and Prakash, Shvetank and Jabbour, Jason and Aleksandra Faust and Guido C. H. E. de Croon and Vijay Janapa Reddi}
}
