The Robotics Revolution, with Physical Intelligence’s Cofounder Chelsea Finn
This week on No Priors, Elad speaks with Chelsea Finn, cofounder of Physical Intelligence and currently Associate Professor at Stanford, leading the Intelligence through Learning and Interaction Lab. They dive into how robots learn, the challenges of training AI models for the physical world, and the importance of diverse data in reaching generalizable intelligence. Chelsea explains the evolving landscape of open-source vs. closed-source robotics and where AI models are likely to have the biggest impact first. They also compare the development of robotics to self-driving cars, explore the future of humanoid and non-humanoid robots, and discuss what’s still missing for AI to function effectively in the real world. If you’re curious about the next phase of AI beyond the digital space, this episode is a must-listen.
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Show Notes:
0:00 Introduction
0:31 Chelsea’s background in robotics
3:10 Physical Intelligence
5:13 Defining their approach and model architecture
7:39 Reaching generalizability and diversifying robot data
9:46 Open source vs. closed source
12:32 Where will PI’s models integrate first?
14:34 Humanoid as a form factor
16:28 Embodied intelligence
17:36 Key turning points in robotics progress
20:05 Hierarchical interactive robot and decision-making
22:21 Choosing data inputs
26:25 Self driving vs robotics market
28:37 Advice to robotics founders
29:24 Observational data and data generation
31:57 Future robotic forms
Shaping the World of Robotics with Chelsea Finn
In the newest episode of Gradient Dissent, Chelsea Finn, Assistant Professor at Stanford's Computer Science Department, discusses the forefront of robotics and machine learning.
Discover her groundbreaking work, where two-armed robots learn to cook shrimp (messes included!), and discuss how robotic learning could transform student feedback in education.
We'll dive into the challenges of developing humanoid and quadruped robots, explore the limitations of simulated environments and discuss why real-world experience is key for adaptable machines. Plus, Chelsea will offer a glimpse into the future of household robotics and why it may be a few years before a robot is making your bed.
Whether you're an AI enthusiast, a robotics professional, or simply curious about the potential and future of the technology, this episode offers unique insights into the evolving world of robotics and where it's headed next.
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Chelsea Finn: how to build AI that can keep up with an always changing world
Chelsea Finn joins Host Pieter Abbeel to discuss distribution shift, meta-learning, editing LLMs, single-life RL, and what can AI not (yet) do today.
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Trends in Reinforcement Learning with Chelsea Finn - #335
Today we continue to review the year that was 2019 via our AI Rewind series, and do so with friend of the show Chelsea Finn, Assistant Professor in the CS Department at Stanford University. Chelsea’s research focuses on Reinforcement Learning, so we couldn’t think of a better person to join us to discuss the topic. In this conversation, we cover topics like Model-based RL, solving hard exploration problems, along with RL libraries and environments that Chelsea thought moved the needle last year.
Episode 19 - Chelsea Finn
This week we return to the world of thinking robots with Chelsea Finn, one of the youngest experts in the field, who talks about her journey, about her work in meta-learning and about lifelong learning for robots.
Robotic Perception and Control with Chelsea Finn - TWiML Talk #29
This week we continue our series on industrial applications of machine learning and AI with a conversation with Chelsea Finn, a PhD student at UC Berkeley. Chelsea’s research is focused on machine learning for robotic perception and control. Despite being early in her career, Chelsea is an accomplished researcher with more than 14 published papers in the past 2 years, on subjects like Deep Visual Foresight , Model-Agnostic Meta-Learning and Visuomotor Learning to name a few, all of which we discuss in the show, along with topics like zero-shot, one-shot and few-shot learning. I’d also like to give a shout out to Shreyas, a listener who wrote in to request that we interview a current PhD student about their journey and experiences. Chelsea and I spend some time at the end of the interview talking about this, and she has some great advice for current and prospective PhD students but also independent learners in the field. During this part of the discussion I wonder out loud if any listeners would be interested in forming a virtual paper reading club of some sort. I’m not sure yet exactly what this would look like, but please drop a comment in the show notes if you’re interested. I'm going to once again deploy the Nerd Alert for this episode; Chelsea and I really dig deep into these learning methods and techniques, and this conversation gets pretty technical at times, to the point that I had a tough time keeping up myself. The notes for this page can be found at twimlai.com/talk/29
