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 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.

  • 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).

Upcoming & Recent

  • Summer 2021: I will be interning with Martin Riedmiller's team at DeepMind.
  • 5 March 2021: I will be speaking on the TalkRL Podcast covering my research and interests. 
  • 4 March 2021: I will be returning (virtually) to give a talk on Model-based RL at Cornell Robotics Seminar.
  • 18 Feb 2021: Some paper updates: 1 was accepted to IEEE Symposium on Technology and Society (here) and 1 more to AISTATS 2021 (page soon).

Older news

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I think about...

Robotics

Intelligent & novel devices to interact with the physical world.

Machine Learning

The science of using data to make decisions in the presence of uncertainty.

Society

Making sure the stakeholders of automation are in the conversation.

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Blogos

All grad students (should) study graphic design

People judge your papers by their cover. You can trick them into believing your science with pretty pictures.

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Exploitation Exploration (in MBRL)

A few lessons from model-based reinforcement learning how exploration can happen through exploitation of some metric.

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Lifelong Learning 2021

What I have been learning from recently.

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More musings →

Papers

AI Development for the Public Interest: From Abstraction Traps to Sociotechnical Risks

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We study three developing subfields of AI research and their growing relationship with the sociotechnical: AI Safety, Fair Machine Learning, and Human-in-the-loop Autonomy.

Nonholonomic Yaw Control of an Underactuated Flying Robot with Model-based Reinforcement Learning

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We explored how MBRL can learn multi-step, nonlinear controllers!

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

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Trying to reframe the MBRL framework with long-term predictions instead of one-step predictions!
More papers →