Tiny Robot Learning: Challenges and Directions for Machine Learning in Resource-Constrained Robots

Sabrina Neuman
Brian Plancher
Bardienus Pieter Duisterhof
Srivatsan Krishnan
Colby R. Banbury
Mark Mazumder
Shvetank Prakash
Jason Jabbour
Guido C. H. E. de Croon
Vijay Janapa Reddi
IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS) special session on Low Power Autonomous Systems(2022) (to appear)


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.