aiqbench analysis
This research introduces LLM-Advisor, a framework leveraging large language models to refine path planning across complex terrains by identifying lower-cost routes missed by traditional global planners. The approach combines semantic terrain context with deterministic validation and fallback mechanisms to ensure feasibility while significantly improving cost efficiency on benchmark datasets like MultiTerraPath. By bridging high-level reasoning with rigorous safety checks, the study demonstrates a practical application of LLMs in solving specific robotics optimization problems rather than acting as general-purpose agents.
LLM-Advisor: An LLM Advisor for Cost-efficient Path Planning across Multiple Terrains
arXiv:2503.01236v3 Announce Type: replace Abstract: This paper addresses fixed-graph terrain-aware path refinement, in which a global planner is restricted to a predefined route space and may remain optimal within that space while missing lower-cost terrain corridors available in the native-resolution map. We propose LLM-Advisor, an external verification-guided multimodal refinement framework that uses semantic terrain context to propose route alternatives with lower cost than the baseline route. The framework combines a multimodal prompt, map-disjoint Reference Example Augmentation (REA), and deterministic validation with fallback: a proposed route is deployed only when it is feasible and strictly lower in cost than the baseline. To evaluate the method, we introduce MultiTerraPath, a controlled 2,000-map benchmark with Easy and Hard subsets for fixed-graph terrain-cost refinement, and further conduct a semantic-cost transfer evaluation on RUGD. Using coarse-lattice A* with stride r=50 as the fixed baseline planner, we compare LLM-Advisor with direct LLM planning and LLM-A*. LLM-Advisor with GPT-5.5 achieves the highest FIR on MultiTerraPath, improving 52.00% of Easy maps and 45.00% of Hard maps. Deterministic verification and fallback yield 100.00% NDR and Final Validity across all evaluated RUGD scene groups. Across lattice strides from r=10 to r=100, offline oracle analysis shows that coarser graphs create larger coarse--oracle gaps, while LLM-Advisor recovers up to 49.16% and 29.98% of the available gap on Easy and Hard maps when r=100, respectively.
Abstract:This paper addresses fixed-graph terrain-aware path refinement, in which a global planner is restricted to a predefined route space and may remain optimal within that space while missing lower-cost terrain corridors available in the native-resolution map. We propose LLM-Advisor, an external verification-guided multimodal refinement framework that uses semantic terrain context to propose route alternatives with lower cost than the baseline route. The framework combines a multimodal prompt, map-disjoint Reference Example Augmentation (REA), and deterministic validation with fallback: a proposed route is deployed only when it is feasible and strictly lower in cost than the baseline. To evaluate the method, we introduce MultiTerraPath, a controlled 2,000-map benchmark with Easy and Hard subsets for fixed-graph terrain-cost refinement, and further conduct a semantic-cost transfer evaluation on RUGD. Using coarse-lattice A* with stride r=50 as the fixed baseline planner, we compare LLM-Advisor with direct LLM planning and LLM-A*. LLM-Advisor with GPT-5.5 achieves the highest FIR on MultiTerraPath, improving 52.00% of Easy maps and 45.00% of Hard maps. Deterministic verification and fallback yield 100.00% NDR and Final Validity across all evaluated RUGD scene groups. Across lattice strides from r=10 to r=100, offline oracle analysis shows that coarser graphs create larger coarse--oracle gaps, while LLM-Advisor recovers up to 49.16% and 29.98% of the available gap on Easy and Hard maps when r=100, respectively.
Robotics (cs.RO); Artificial Intelligence (cs.AI)
arXiv:2503.01236 [cs.RO]
(or arXiv:2503.01236v3 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2503.01236 Focus to learn more arXiv-issued DOI via DataCite
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