New labs

Chelsea Finn

Physical Intelligence and Stanford University

Why we track: Robot foundation models

Career moves

No published career moves.

Papers

Paper2026-10-29 · arXiv (Cornell University)

A technology for jointly training robot data using diverse controllers

Cross-Controller Transfer via In-Context Behavior Adaptation

Chelsea Finn +2 more

What: Developed a technology to utilize data from robots using different control methods for learning without conflicts.
Key result: The average success rate improved by 33 percentage points compared to conventional methods.
Why it matters: Demonstrates the potential to more effectively utilize robot data collected from diverse environments.AI summary

Robotics & physical AI

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Paper2026-10-05 · arXiv (Cornell University)

A Performance-Guided Data Collection Method to Enhance Robot Learning Efficiency

Mulligan: Performance-Guided Data Collection for Efficient On-Robot Learning

Chelsea Finn +6 more

What: Researchers proposed Mulligan, a technique that improves efficiency in robot manipulation learning by collecting data centered around failed and untried states.
Key result: The Mulligan technique improved the robot's final task success rate from 10 percent to 34 percent compared to conventional methods.
Why it matters: It demonstrates a technological advancement that helps human trainers use time and effort more efficiently when teaching robots.AI summary

Robotics & physical AI

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Paper2026-10-01 · arXiv (Cornell University)

A 'Finding Inspiring Papers' Benchmark Created by 184 Researchers

ScholarCatalyst: A Benchmark for Retrieving Papers That Inspire New Research

Chelsea Finn +13 more

What: We created ScholarCatalyst, a dataset where first authors of 207 recent computer science papers indicated which prior papers actually helped their research, and presented a retrieval task to find them.
Key result: Agentic search (AI that iteratively performs tasks on its own) did not show better performance than basic search methods (recall of 0.42 versus 0.48 based on the top 20 results). Agents based on Claude Fable 5.1 only reached 0.51.
Why it matters: This indicates that current AI still lacks the expert intuition needed to identify necessary prior ideas from vast research literature, pointing to the need for new training methods. While this can be viewed as a step forward for AI assisting scientific research, actual commercialization or industrial effects cannot be confirmed from the abstract.AI summary

Agents & coding AI for science

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This page compiles published papers, official announcements and press reports for information only. Paper summaries are written by AI from the abstract and reviewed by the editor; they may differ from the original, so check the paper for anything important. Nothing here is a recommendation to buy or sell any security or investment advice. Career moves cite official announcements or major press reports; some career records come from Wikidata (CC0). Summaries on this page were translated by AI from the editor-reviewed Korean version.