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LLM-Guided Future Hypotheses for Horizon-Aware Exploration in Multi-Step Robot Manipulation

The paper proposes Future-Experience Conditioning (FEC), which uses a latent representation of a short future video as a condition for closed-loop policies, for exploration and policy adaptation in multi-step robot manipulation. Future clips are generated in three stages: an LLM reasoner performs task-ontology reasoning initialized from the current scene state, a robot-free digital twin rolls out the intended object motion, and a mask-free video diffusion model synthesizes a robot-consistent future clip.

Part of the event: FEC: Conditioning Robot Policies on Latent Representations of Future Video

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