Robot-Use Agents: Why General-Purpose Models May Win in Robotics

One of the biggest surprises in AI over the last few years has been how well coding agents generalize beyond software. In a recent essay, MIT professor Philip Isola argued that we may be entering the era of robot-use agents: general-purpose models that can control different robots, write policies, and learn new physical tasks with little or no robot-specific training. In this episode of Decoded, we're joined by the founders of Waddle Labs and RoboCurve, two of the startups whose work helped drive this realization. They're working at the frontier of using general-purpose models to control robots, and their recent demos helped inspire the growing conversation around robot-use agents. Together, we dig into the research behind that idea, from code-as-policies and vision-language-action models to the harnesses and evals needed to make these systems work in the real world. Chapters: 00:00 — Intro + The Rise Of Robot Use Agents 04:07 — The Bitter Lesson for Robotics 07:40 — From Coding Agents to Robot Policies 10:41 — In-Context Learning vs Model Training 14:22 — Building a Harness for Robot Control 17:50 — Turning Robot Actions Into Reusable Skills 20:55 — How AI Models Learn the Physical World 26:05 — How Close Are We To General Purpose Robots? https://www.waddlelabs.ai/ https://robocurve.org/ Apply to Y Combinator: https://www.ycombinator.com/apply Work at a startup: https://www.ycombinator.com/jobs

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