• Robotic learning researcher (spends some time as a scientific writer).
  • GOAL: to understand and develop safe and societally beneficial autonomous systems.
  • CURRENTLY: Ph.D. Candidate at UC Berkeley EECS.
  • PAST: Cornell ECE `17; Intern at DeepMind, Facebook AI Research, Tesla.

About me

Hello! I am a PhD Candidate at the University of California, Berkeley, Department of Electrical Engineering and Computer Sciences, advised by Professor Kristofer Pister in the Berkeley Autonomous Microsystems Lab, and pseudo-advised by Roberto Calandra at Facebook AI Research!

Hello! I am a PhD Candidate at the University of California, Berkeley studying the intersection of robotics and machine learning. I am a member of the Department of Electrical Engineering and Computer Sciences, advised by Professor Kristofer Pister in the Berkeley Autonomous Microsystems Lab, and pseudo-advised by Roberto Calandra at Facebook AI Research! I am actively involved in outreach and inclusion efforts and an advocate for mental health -- he is the EEGSA wellness chair and founder of the UC Berkeley Equal Access to Application Assistance program.

Prior to UC Berkeley, I was a proud member of Cornell Electrical and Computer Engineering 2017 where I learned to do research with the Lab of Plasma Studies and the SonicMEMs Lab. I bring my research foundation in hardware, models, and physics to the data-driven world of machine learning. At Cornell, I was a part of Cornell Lightweight Rowing. I did an internship with Tesla Motors Battery Engineering in 2015.

I am happy to be a product of The Ocean State.

Nathan Lambert is  a PhD Candidate at the University of California, Berkeley working at the intersection of machine learning and robotics. He is a member of the Department of Electrical Engineering and Computer Sciences, advised by Professor Kristofer Pister in the Berkeley Autonomous Microsystems Lab. Nathan has worked extensively with Roberto Calandra at Facebook AI Research and is joining DeepMind Robotics remotely for the summer of 2021. During his Ph.D., he was awarded the UC Berkeley EECS Demetri Angelakos Memorial Achievement Award for Altruism.

  • I am always looking to work with and promote under-represented groups in STEM fields. If you relate to this, please email me directly if you're curious in my work or trying to find a project in AI at UC Berkeley (I have mentored and advised multiple students through teaching at Berkeley).
  • Online mentor at Polygence (currently only taking scholarship-based students from under-represented groups).
  • Creator of Democratizing Automation newsletter & blog on making the future of AI and robotics equitable.

I like to try and have fun between my many projects. You can find me on Strava, I also happen to be a brand ambassador for Picky Bars. I actively track my health, cook, and read (recipe and book pages in construction).

News

  • 20 March 2022: one of the last papers of my Ph.D. is out -- studying compounding prediction error in MBRL! [link]
  • 8 February 2022: our long-coming white-paper on the integration of RL and society is out from the Center for Long-term Cybersecurity! [link]
  • 27 January 2022: I was lucky to be interning with Martin Riedmiller's team at DeepMind, here's our paper. [link]
  • 1 September 2021: we released a new paper on our simulator, BotNet, for studying high-agent count network control (best paper award finalist)! [link]
  • 27 April 2021: I was awarded the UC Berkeley EECS Demetri Angelakos Memorial Achievement Award for Altruism. [link]
  • 25 April 2021: I had another paper on sociotechnics in AI published in IEEE Transactions on Technology and Society. [link]
  • 21 April 2021: We released an open-source library for model-based reinforcement learning. [link]
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Robotics

Intelligent & novel devices to interact with the physical world.

Machine Learning

The science of using data to decide in the presence of uncertainty.

Society

Making sure the stakeholders of automation are in the conversation.

Blogos

The last reliable path into AI

A confluence of trends leaves AI+something, rather than pure AI, as the last great path into machine learning research.

Read more...

ML/RL & Microrobotics

A memo I wrote to my research group on the open questions when applying machine learning to another research area: novel microrobotics.

Read more...

Debugging Deep Model-based Reinforcement Learning Systems

Things I have learned in 3 years of a young, and generally tricky research field.

Read more...
More musings →

Papers

Choices, Risks, and Reward Reports: Charting Public Policy for Reinforcement Learning Systems

Learn more.
We detail why reinforcement learning systems pose a different type of (dynamic) risks to society. This paper outlines the different types of feedback present in RL systems, the risks they pose, and a path forward for policymakers.

The Challenges of Exploration for Offline Reinforcement Learning

We flip the script on Offline RL research and ask the question of "what is the best dataset to collect?" rather than "what is the best algorithm?"

Investigating Compounding Prediction Errors in Learned Dynamics Models

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In this paper we set out to understand the causes of compounding prediction errors in one-step learned models. With this, we hope a next generation of models can be used to improve model-based reinforcement learning.
More papers →