Learning Visual Spatial Planning from Symbolic State via Modality-Gap-Aware Self-Distillation
arXiv:2606.06076v1 Announce Type: new Abstract: While vision-language models excel at general multimodal understanding, they still struggle with visual spatial planning. We attribute this to a perception-reasoning modality gap: visual planning requires models to infer latent state structures from pixels and then reason over the recovered structure to produce valid actions, whereas symbolic planning directly leverages explicit objects and constraints. This creates dual bottlenecks in visual state recovery and multi-step planning. To address this, we propose MGSD, a two-stage modality-gap-aware
The paper identifies a crucial limitation in current vision-language models, specifically their struggle with visual spatial planning, and proposes a new methodology to address it, indicating active research progress in bridging model capabilities.
Improving visual spatial planning in AI systems would significantly enhance their ability to interact with and navigate complex real-world environments, leading to more robust and autonomous AI applications.
This research introduces a novel modality-gap-aware self-distillation technique (MGSD), which could improve how AI models interpret and reason about visual information for planning, moving beyond current multimodal understanding limitations.
- · AI research institutions
- · Robotics companies
- · Generative AI developers
- · Automation sector
- · Companies relying on brittle, non-spatial AI
- · Legacy AI planning systems
AI systems will become more adept at understanding and manipulating physical spaces based on visual input.
This improved spatial reasoning will accelerate the development of advanced robotic systems for manufacturing, logistics, and assistive care.
The integration of such sophisticated planning capabilities into general-purpose AI agents could enable broader autonomy across various industries, creating more adaptive and less human-dependent systems.
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Read at arXiv cs.AI