Responsible Tiny Robots
Responsible Tiny Robots
Project Overview
Many emerging robotics use cases will require small, cheap robots that use embedded devices (e.g., MCUs) for computation. When compressing robotics algorithms to fit on these resource constrained computational devices, 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 hardware-software co-design. At the same time, we need to ensure that the proliferation of mobile edge intelligence is done responsibly to ensure that these advances benefit all.
Publications
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(2024). Differentially Encoded Observation Spaces for Perceptive Reinforcement Learning. In ICRA 2024.
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(2024). AI in the Developing World: How ‘Tiny Machine Learning’ can have a Big Impact. In The Conversation.
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(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.
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(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
- Benjamin Brown
- Brian Plancher
- Colby Banbury
- Elakhya Nedumaran
- Emanuel Moss
- Guido C.H.E. de Croon
- Jason Jabbour
- Jim MacArthur
- Joao Santos
- Khai Nguyen
- Lev Grossman
- Marco Zennaro
- Mark Mazumder
- Matthew Stewart
- Mona Sloane
- Nat Jeffries
- Pete Warden
- Sabrina M. Neuman
- Sachin Katti
- Sam Schoedel
- Shawn Hymel
- Shvetank Prakash
- Srivatsan Krishnan
- Susan Kennedy
- Vijay Janapa Reddi
- Yasmine Omri
- Zachary Manchester
