Chelsea Finn is an Assistant Professor at Stanford and part of the Google Brain team. She's interested in the capability of robots and other agents to develop broadly intelligent behavior through learning and interaction at scale. In this episode, we chat about some of the biggest bottlenecks in RL and robotics—including distribution shifts, Sim2Real, and sample efficiency—as well as what makes a great researcher, why she aspires to build a robot that can make cereal, and much more.

In this episode of Gradient Dissent, Chelsea Finn, Assistant Professor at Stanford's Computer Science Department, discusses the forefront of robotics and machine learning. Topics include two-armed robots learning to cook shrimp, the challenges of developing humanoid and quadruped robots, the limitations of simulated environments, and the future of household robotics. She also talks about her work on student feedback in education and the impact of AI coding tools on computer science education.

Sergey Levine and Chelsea Finn from Physical Intelligence discuss a realistic path to robotic foundation models, key factors for the future of robotics, and the transformerification of robotics. They explore the shift towards horizontal robotics companies and the importance of building general robotics models for various tasks.