SOURCE-LINKED INTELLIGENCE
Decoupling Planning and Control for Instructable Agents
Recent work shows that pre-trained, instruction-tuned vision-language models (VLMs) perform well at mapping from instructions and observations to high-level plans, but struggle to realize such plans as reliable low-latency action sequences in unfamiliar environments. At the same time, world-model controllers excel at fast observation-to-action control, but lack open-ended task guidance. In this work, we combine these strengths into a single system, Instruct-to-Act, where we train a world-model controller to act autonomously at high frequency when conditioned on sparse, higher-latency, and high
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-08-27T08:17:58.000Z
First collected: 2026-09-21T08:51:59.673Z. This is not the publication date.