A central challenge in #physical #AI is data scarcity: vision-language-action (#VLA) models are fundamentally limited by the availability of high-quality robotics demonstrations.
In our recent work, we introduce R&B-EnCoRe (arxiv.org/pdf/2602.08167), a framework that enables models to self-bootstrap embodied #reasoning by leveraging synthetic visuo-textual data together with limited embodiment-specific experience. In essence, R&B-EnCoRe allows models to learn how to reason in an embodied setting.
Our approach treats reasoning as a latent variable and uses self-supervised refinement to learn reasoning strategies that are directly predictive of successful control—without human annotations, reward engineering, or external verifiers.
We validate the approach across a range of embodiments—including manipulation, navigation, and autonomous driving—and across model scales from 1B to 30B parameters, observing consistent improvements:
💪 +28% task success in real-world manipulation
🦿 +101% score in legged locomotion navigation
🚗 −21% collision rate in autonomous driving
Overall, this work highlights a promising direction: aligning internet-scale priors with embodiment-specific data to enable scalable, self-improving physical intelligence.
Kudos to an amazing team: Milan Ganai Katie Luo @JonasFrey96 Clark Barrett
🌐 Website: milanganai.github.io/rnb-encore/
📄 Paper: arxiv.org/pdf/2602.08167
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