Interpretability and Safety for Robot Foundation Models

At our inaugural YCML at Startup School, YC Partner Ankit Gupta speaks with Bear Häon about applying interpretability and AI safety techniques to vision-language-action models. The work examines activations inside a vision-language-action model, groups neurons associated with concepts such as speed or caution, and then steers the robot's behavior by amplifying those groups. This provides a way to better understand and control how robots translate language into physical actions. The broader goal is to develop safety methods for physical AI, where failures can have direct consequences in the real world. Apply to Y Combinator: https://www.ycombinator.com/apply Work at a startup: https://www.ycombinator.com/jobs

Ankit GuptahostBear Häonguest

Aug 6, 20265mWatch on YouTube ↗

EPISODE INFO

Released: August 6, 2026
Duration: 5m
Channel: YC Root Access
Watch on YouTube: ▶ Open ↗

EPISODE DESCRIPTION

At our inaugural YCML at Startup School, YC Partner Ankit Gupta speaks with Bear Häon about applying interpretability and AI safety techniques to vision-language-action models. The work examines activations inside a vision-language-action model, groups neurons associated with concepts such as speed or caution, and then steers the robot's behavior by amplifying those groups. This provides a way to better understand and control how robots translate language into physical actions. The broader goal is to develop safety methods for physical AI, where failures can have direct consequences in the real world. Apply to Y Combinator: https://www.ycombinator.com/apply Work at a startup: https://www.ycombinator.com/jobs

SPEAKERS

Ankit Gupta
host
Host at YC Root Access (Y Combinator) who interviews guests about machine learning and startups.
Bear Häon
guest
Founder of the Physical AI Safety Institute and researcher focused on interpretability and safety for robot foundation models.

EPISODE SUMMARY

In this episode of YC Root Access, featuring Ankit Gupta and Bear Häon, Interpretability and Safety for Robot Foundation Models explores interpreting robot foundation models to control behavior and improve safety The discussion presents one of the first interpretability papers for Robot Foundation Models (RFMs), adapting AI-safety interpretability techniques to embodied, physically acting systems.

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