APIVOT: Adaptive Planning with Interleaved Vision-Language Thoughts
APIVOT is a VLM-based planner that adaptively interleaves language and visual thoughts for long-horizon robot planning, achieving significant gains in spatially constrained kitchen tasks.
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[Submitted on 9 Jul 2026]
Title:APIVOT: Adaptive Planning with Interleaved Vision-Language Thoughts
View a PDF of the paper titled APIVOT: Adaptive Planning with Interleaved Vision-Language Thoughts, by Emily Jin and 5 other authors
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Abstract:Long-horizon robot planning requires jointly reasoning over semantic task structure and geometric feasibility. To successfully execute a task, a robot must decompose goals, select task-relevant objects, and sequence actions, while ensuring that plans satisfy spatial constraints such as limited free space and object collisions. In this work, we propose APIVOT, a VLM-based planner that adaptively interleaves language and visual thoughts for long-horizon planning. APIVOT learns to leverage language for semantic reasoning, while using visual thoughts as imagined future states for internal verification of geometric feasibility. On long-horizon kitchen tasks, APIVOT outperforms general-purpose VLMs and prior planning frameworks, achieving the largest gains in spatially constrained settings. We find that APIVOT learns meaningful modality selection behavior, demonstrating that adaptive interleaving of vision-language thoughts improves both planning success and reasoning efficiency.
Comments: Project Page: this https URL
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Robotics (cs.RO)
Cite as: arXiv:2607.08024 [cs.CV]
(or arXiv:2607.08024v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2607.08024
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Emily Jin [view email] [v1] Thu, 9 Jul 2026 01:02:35 UTC (4,202 KB)
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