Hierarchical planning over multi-scale latent world models enables 70% success on real robotic pick-and-place with goal-only input where flat models achieve 0%, while cutting planning compute up to 4x in simulations.
Foundation policies with hilbert representations
2 Pith papers cite this work. Polarity classification is still indexing.
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cs.LG 2years
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Robust minimax task inference in BFMs achieves dynamics-shift robustness from nominal offline data alone and outperforms standard baselines.
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Hierarchical Planning with Latent World Models
Hierarchical planning over multi-scale latent world models enables 70% success on real robotic pick-and-place with goal-only input where flat models achieve 0%, while cutting planning compute up to 4x in simulations.
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When Dynamics Shift, Robust Task Inference Wins: Offline Imitation Learning with Behavior Foundation Models Revisited
Robust minimax task inference in BFMs achieves dynamics-shift robustness from nominal offline data alone and outperforms standard baselines.