CLAMP: Constrained Decoding for Vision-Language Embodied Planning
Quick summary
arXiv:2609.08602v1 Announce Type: new Abstract: Embodied planning increasingly relies on vision-language models (VLMs) to translate instructions and visual observations into executable action sequences. However, fluent plans are not always executable. A VLM may refer to objects that are not visually observed, select actions whose required affordances are unavailable, or violate syntax and action constraints. We introduce CLAMP, a multimodal constraint-grounding framework that turns scene evidence into decoding-time constraints for a frozen VLM planner. CLAMP uses the initial observation to res
Key takeaways
- arXiv:2609.08602v1 Announce Type: new Abstract: Embodied planning increasingly relies on vision-language models (VLMs) to translate instructions and visual observations into executable action sequences.
- However, fluent plans are not always executable.
- A VLM may refer to objects that are not visually observed, select actions whose required affordances are unavailable, or violate syntax and action constraints.
Why it matters
“CLAMP: Constrained Decoding for Vision-Language Embodied Planning” shows why continuity and fallback planning matter as AI services move into operational workflows. Provider status, fault tolerance, alternate paths and user communication should be part of production design.
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