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Trying to think freely and create equitable & impactful automation @ UCBerkeley EECS. Subscribe directly at robotic.substack.com. More at natolambert.com

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What I do:

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Where you will find me:


How behaving as a digital platform rather than a manufacturer will be the sweet spot for the next generation of robotics companies

Data and deep learning have been changing the world for the last decade. Most robotics companies have not tapped into the datavolution and the mainstream vision for what robotics can be is still far from realized. The status quo for robotics companies will not achieve that dream of robots helping us with any task in any scenario (or at least trying to some degree of success), but it will create some expensive toys that are solving one problem (hint, we have some already).

The most popular robotics startups these days are the likes of Boston Dynamics, Tesla Motors, and maybe…


Making Sense of Big Data

Where a hot new research sub-field is going in RL.

As many of you know, I am doing my PhD centered around model-based reinforcement learning (MBRL). This post is not talking about the technical details and recent work, but rather why I am bullish on it for the future. Beyond the prospects of how well it can perform (it’s much younger than most of deep RL), having discussions with AI Safety and Ethical AI experts makes it clear that it’s more structured learning-setup is pointing towards systems that humans can better understand.


Lessons about AI from lessons about our mind. Focused on the nature of free will.

I have been trying to train my mind through meditation for about two years now. It is remarkable to me the frequency and ease by which you can notice the actual modus operandi of the brain differing from the perceived modus operandi. Things like superimposing images on your visual field, noticing how arbitrary choices are made (they appear), and the transient nature of even self-conjured negative emotions all can easily challenge our status quo.

The Waking Up App has a new series concisely dictating the illusion that is the fleeting feeling of free will. It is unsurprising that how we…


I tried my best to absorb a lot of content at NeurIPs 2020, and it was just as overwhelming as ever. Everyone makes decisions of what content they want to focus on, and it is always an exploration (learn new things) versus exploitation (further mastering material in your area of expertise) tradeoff. I chose to focus on my areas of expertise: model-based learning, RL, and robotics (I also spent a good bit networking, but that happened in between the lines of these notes). Some of these talks require registration, but public links may become available.

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Source — Author, Appalachian Trail.

Workshops

Some of my mentors have…


A small step into the research community’s most opaque framework.

Reinforcement learning (RL) — the framework of interacting with an environment to learn a policy to act and achieve some objective — is on the up in many domains. Some domains make sense, some will never work, and most are bound to fall somewhere in the middle. RL is so popular because of its elegance: we know that most creatures learn by interacting with their environment (but, we should note that evolutions are really a strong prior on these behaviors that we don’t know how to model). …


Abstraction & scale & ethical systems.

What does it mean to be a roboticist? The somewhat cliche nature of the title, epitomized by the numerous startup C-suite employees with subtitles “PhD in Robotics,” leads to a more challenging problem of defining ethics guides. The burden is on me to develop rank-ordered guidelines for what people should consider in a broad sense — and therein be educated in. The actual problems where these will be applied are in its nature flexible, so therefore the principles should be reordered. …


A new “subfield” founded in 2019 is making waves, and is more accessible than I first thought

With the devolving world order, seeking positive, engaging work, I wanted to learn a) what actually is machine learning for climate change and b) are there reasonable paths for us to dive in and contribute? To quote the call for action in a paper I cite heavily later:

Groundbreaking technologies have an impact, but so do well-constructed solutions to mundane problems.

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Recent work

To start with, I knew there were a bunch of recent workshops on climate change and machine learning (such as ICLR 2020, ICML 2019, NeurIPs 2019 editions). When looking here, it turns out it is a centralized group of…


How electrical engineers see the world: Models, Systems, Code; and Robots

The best teams for robotics are not all computer scientists — they have electrical & mechanical engineers, computer scientists, robots, and more to fill the cracks. This post is an exploration of how different ways of thinking contribute in robotics — and by extension to many software engineering projects.

How would you summarize the overarching conceptual theme of your undergraduate major?

This was originally posted on my free newsletter on robotics & automation, Democratizing Automation.

Seeing models

I don’t characterize EE primarily by circuit design nor nano-fabrication. It took me a long time to figure out what was different between my degree…


Re-thinking robot design: don’t restrict your robot worldview to robot (a) does task (b).

In this post, I address two questions:

I am intentionally avoiding a couple of hot topics in reinforcement learning that pertains to generalization/task transfer (such as meta-learning or other generalizable methods). I think that most generalization to date is proportional to data-distribution coverage of multiple tasks — not the ability for the same hyperparameters to solve numerically distinct tasks.

The good news: I think expanding…

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