arXiv AI
Language-Conditioned World Modeling for Visual Navigation
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Towards Unified World Models for Visual Navigation via Memory-Augmented Planning and Foresight
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Traj-VLN: Learning Pixel-Space Interaction via Autoregressive Trajectory Generation
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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.