Chelsea Finn

American computer scientist at Stanford University and a co-founder of Physical IntelligencePhysical Intelligence, prop. n.: A San Francisco company, founded in 2024, that builds general-purpose AI 'brains' meant to control many different kinds of robots doing many different tasks. (2024), known for meta-learningmeta-learning, n.: Training a system across many different tasks so that it gets better at learning itself, letting it pick up a new task quickly from only a few examples. and robot learning. As a doctoral student of Pieter AbbeelPieter Abbeel, prop. n.: A Belgian-American roboticist at Berkeley whose research taught robots to learn from demonstrations and from trial and error, and who co-founded the robotics company Covariant. and Sergey LevineSergey Levine, prop. n.: A Berkeley computer scientist, one of the most prolific researchers on how robots can learn skills from data and experience, and a co-founder of the robotics company Physical Intelligence. at Berkeley she co-developed end-to-end deep visuomotor policies for robots and introduced model-agnostic meta-learningmodel-agnostic meta-learning, n.: A training method that searches for a starting point for a model's internal settings from which it can learn any new task of a given kind with just a few small adjustments. (MAML, 2017), which trains a model's initial parameters so that a few gradient steps on a handful of examples adapt it to a new task; her thesis received the ACM Doctoral Dissertation Award. At Stanford, where she has taught since 2019, her group studies how robots can learn broadly and adapt quickly, including the low-cost ALOHA teleoperation system and action chunkingaction chunking, n.: Having a robot's AI plan a short burst of upcoming movements, say the next second, all at once instead of one tiny motion at a time, which makes motion smoother and mistakes rarer. with transformers (2023, with Tony Zhao and others), and she co-authored direct preference optimizationdirect preference optimization, n.: A simpler way to teach an AI people's preferences: instead of building a separate scoring model, it trains directly on pairs of better and worse answers, making the better ones more likely. (2023). Physical Intelligence develops robot foundation modelsrobot foundation model, n.: One large AI model trained on huge amounts of robot and internet data so that it can control many robots on many tasks, and be adapted quickly to new ones. such as π0π0, prop. n.: A general-purpose AI model for controlling robots, released in 2024 by the start-up Physical Intelligence, that sees through cameras, follows typed or spoken instructions and moves robot arms deftly enough to fold laundry..