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ViPlan: A Benchmark for Visual Planning with Symbolic Predicates and Vision-Language Models

arXiv – CS AI|Matteo Merler, Nicola Dainese, Minttu Alakuijala, Giovanni Bonetta, Pietro Ferrazzi, Yu Tian, Bernardo Magnini, Pekka Marttinen|March 4, 2026 at 05:00 AM|1 views

🤖AI Summary

Researchers introduce ViPlan, the first benchmark for comparing Vision-Language Model planning approaches, finding that VLM-as-grounder methods excel in visual tasks like Blocksworld while VLM-as-planner methods perform better in household robotics scenarios. The study reveals fundamental limitations in current VLMs' visual reasoning abilities, with Chain-of-Thought prompting showing no consistent benefits.

Key Takeaways

→ViPlan is the first open-source benchmark to compare VLM-grounded symbolic planning with direct VLM planning methods.
→VLM-as-grounder approaches solved 46% of Blocksworld tasks compared to 9% for direct VLM planning methods.
→In household robotics tasks, VLM-as-planner methods significantly outperformed VLM-as-grounder approaches (34% vs 5% success rate).
→Chain-of-Thought prompting showed no consistent benefits across methods, highlighting persistent VLM limitations.
→The benchmark reveals that different planning approaches excel in different domains based on visual complexity and linguistic knowledge requirements.

#vision-language-models #symbolic-planning #ai-benchmarks #visual-reasoning #robotics #blocksworld #vlm #planning-algorithms #ai-research

Read Original →via arXiv – CS AI

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