Hugging Face Trending Papers
CLAMP: Constrained Decoding for Vision-Language Embodied Planning
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arXiv AI
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CLAMP: Constrained Decoding for Vision-Language Embodied Planning
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 sequenc...
Hugging Face Trending Papers
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Neurosymbolic Embodied Agents
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Cortex: A Bidirectionally Aligned Embodied Agent Framework for Long-horizon Manipulation
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arXiv AI
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Hugging Face Trending Papers
Aug 27
Decoupling Planning and Control for Instructable Agents
The paper introduces Instruct-to-Act, a system that decouples high‑level planning from low‑latency control by combining a vision‑language model (VLM) planner with a world‑model controller. The VLM generates sparse, high‑level text instructions, while the controller executes them autonomously at high frequency. Experiments across seven embodied environments, including multi‑agent settings, show that this approach outperforms both controller‑only and direct VLM action‑generation methods, maintains fast control, and allows swapping in different pretrained VLM planners without fine‑tuning.