Next Generation Intelligence for Tiny Robots
Next Generation Intelligence for Tiny Robots
Project Overview
Small, low-cost, globally-accessible robots face stringent power, memory, and compute limitations, historically preventing the use of sophisticated algorithms. When compressing robotics algorithms to fit on the embedded systems running on these robots (e.g., MCUs), new challenges and opportunities emerge. We are working to unlock the full potential of these tiny robots by leveraging insights from robotics, machine learning, and computer architecture / embedded systems to custom tailor algorithmic solutions through co-design.
Publications
-
(2024). Optimizing at All Scales: Edge (Non)linear Model Predictive Control from MCUs to GPUs. In Frontiers of Optimization at RSS 2024.
-
(2024). Differentially Encoded Observation Spaces for Perceptive Reinforcement Learning. In ICRA 2024.
-
(2024). AI in the Developing World: How ‘Tiny Machine Learning’ can have a Big Impact. In The Conversation.
-
(2023). Tiny Robot Learning: Expanding Access to Edge ML as a Step Toward Accessible Robotics. In Workshop on Lowering Barriers for Robotics Research at RSS 2023.
-
(2022). Closing the Sim-to-Real Gap for Ultra-Low-Cost, Resource-Constrained, Quadruped Robot Platforms. In Workshop on Closing the Sim2Real Gap at RSS 2022.
Collaborators
- Aleksandra Faust
- Anoushka Alavilli
- Bardienus Duisterhof
- Brian Plancher
- Colby Banbury
- Elakhya Nedumaran
- Emre Adabag
- Guido C.H.E. de Croon
- Jason Jabbour
- Khai Nguyen
- Lev Grossman
- Marco Zennaro
- Mark Mazumder
- Matthew Stewart
- Miloni Atal
- Sabrina M. Neuman
- Sam Schoedel
- Shvetank Prakash
- Srivatsan Krishnan
- Vijay Janapa Reddi
- William Gerard
- Xueyi Bu
- Zachary Manchester
