Visual-Language-Guided Task Planning for Horticultural Robots

• URL: http://arxiv.org/abs/2601.11906v1
• Date: Sat, 17 Jan 2026 04:45:31 GMT
• Title: Visual-Language-Guided Task Planning for Horticultural Robots
• Abstract summary: We introduce a novel, modular framework that uses a Visual Language Model (VLM) to guide robotic task planning.<n>We contribute a benchmark for short- and long-horizon crop monitoring tasks in monoculture and polyculture environments.
• Score: 7.247976293510842
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Crop monitoring is essential for precision agriculture, but current systems lack high-level reasoning. We introduce a novel, modular framework that uses a Visual Language Model (VLM) to guide robotic task planning, interleaving input queries with action primitives. We contribute a comprehensive benchmark for short- and long-horizon crop monitoring tasks in monoculture and polyculture environments. Our main results show that VLMs perform robustly for short-horizon tasks (comparable to human success), but exhibit significant performance degradation in challenging long-horizon tasks. Critically, the system fails when relying on noisy semantic maps, demonstrating a key limitation in current VLM context grounding for sustained robotic operations. This work offers a deployable framework and critical insights into VLM capabilities and shortcomings for complex agricultural robotics.

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