Flex-π: Teach Geometry, Choose How Much to Imagine
A 6B-parameter world-action model that jointly predicts 3D pointmaps and DINO features alongside RGB frames, improving demonstration efficiency, generalization, and long-horizon task performance in robotics. Integrating structured 3D world predictions into the action loop represents a meaningful advance in robot foundation model design. In this episode of Embodied AI 101, we explore "Flex-π: Teach Geometry, Choose How Much to Imagine". We break down the research, methodology, and real-world implications for robotics, AI, and physical intelligence. Embodied AI 101 covers the latest research at…
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