BISON learns bilevel policies over symbolic world models to generalize long-horizon robotic planning beyond VLA and end-to-end baselines while remaining efficient even at 10,000-object scale.
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StableVLA adds an Information Bottleneck Adapter to VLA models that improves robustness to visual corruptions by 30% on average with under 10M extra parameters and no extra data, even when using a much smaller backbone.
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Learning Bilevel Policies over Symbolic World Models for Long-Horizon Planning
BISON learns bilevel policies over symbolic world models to generalize long-horizon robotic planning beyond VLA and end-to-end baselines while remaining efficient even at 10,000-object scale.
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StableVLA: Towards Robust Vision-Language-Action Models without Extra Data
StableVLA adds an Information Bottleneck Adapter to VLA models that improves robustness to visual corruptions by 30% on average with under 10M extra parameters and no extra data, even when using a much smaller backbone.