Robotics

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Embodied intelligence, today!

Robotics is likely the biggest category of my work. It encompasses everything from hardware development to algorithmic studies all with the central goal of creating agents that can better interact with our physical world (even in indirect ways, like traffic lights).

I have so many open areas of research here, and am open to inquiries.

BotNet: A Simulator for Studying the Effects of Accurate Communication Models on Multi-agent and Swarm Control

Sep 1, 2021
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Mark Selden, Jason Zhou, Felipe Campos, Nathan Lambert, Daniel Drew, Kristofer S. J. Pister
IEEE International Symposium on Multi-Robot and Multi-Agent Systems
A simulator for studying high-agent-count networked systems!
[pdf][arxiv][code][video]Full Page

Learning Accurate Long-term Dynamics for Model-based Reinforcement Learning

Dec 16, 2020
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Nathan O Lambert, Albert Wilcox, Howard Zhang, Kristofer SJ Pister, Roberto Calandra
Arxiv
Trying to reframe the MBRL framework with long-term predictions instead of one-step predictions!
[pdf][arxiv][code][video]Full Page

Learning for Microrobot Exploration: Model-based Locomotion, Robust Navigation, and Low-Power Deep Classification

Jul 13, 2020
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Nathan Lambert, Fahran Toddywala, Brian Liao, Eric Zhu, Lydia Lee, Kristofer S.J. Pister
2020 Conference on Manipulation, Automation and Robotics at Small Scales (MARSS)
A collections of steps towards a data-driven autonomous microrobot.
[pdf][arxiv][code][video]Full Page

Learning Generalizable Locomotion Skills with Hierarchical Reinforcement Learning

Sep 26, 2019
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Tianyu Li, Nathan Lambert , Roberto Calandra , Franziska Meier , Akshara Rai
2020 IEEE International Conference on Robotics and Automation (ICRA)
Learning how to walk with a real-world hexapod using a hierarchy of model-free RL for basic motion primitives with model-based RL for higher level planning.
[pdf][arxiv][code][video]Full Page

Low Level Control of a Quadrotor with Deep Model-Based Reinforcement Learning

Jan 11, 2019
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Nathan Lambert, Daniel Drew, Joseph Yaconelli, Roberto Calandra, Sergey Levine, Kristofer Pister
IEEE Robotics and Automation Letters
We used deep model-based reinforcement learning to have a quadrotor learn to hover from less than 5 minutes of all experimental training data.
[pdf][arxiv][code][video]Full Page

Toward Controlled Flight of the Ionocraft: A Flying Microrobot Using Electrohydrodynamic Thrust With Onboard Sensing and No Moving Parts

Jul 18, 2018
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Daniel Drew, Nathan Lambert, Craig Schindler, Kristofer SJ Pister
IEEE Robotics and Automation Letters
A collection of steps towards controlled flight of The Ionocraft, a completely silent microrobot with ion thrust!
[pdf][arxiv][code][video]Full Page
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Research Directions

Novel Robots
Reinforcement Learning
Multi-Agent Systems
Beneficial AI
Robotics
Model Learning