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MapGPT: Map-Guided Prompting with Adaptive Path Planning for Vision-and-Language Navigation
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Embodied agents equipped with GPT as their brains have exhibited extraordinary decision-making and generalization abilities across various tasks. However, existing zero-shot agents for vision-and-language navigation (VLN) only prompt GPT-4 to select potential locations within localized environments, without constructing an effective "global-view" for the agent to understand the overall environment. In this work, we present a novel map-guided GPT-based agent, dubbed MapGPT, which introduces an online linguistic-formed map to encourage global exploration. Specifically, we build an online map and incorporate it into the prompts that include node information and topological relationships, to help GPT understand the spatial environment. Benefiting from this design, we further propose an adaptive planning mechanism to assist the agent in performing multi-step path planning based on a map, systematically exploring multiple candidate nodes or sub-goals step by step. Extensive experiments demonstrate that our MapGPT is applicable to both GPT-4 and GPT-4V, achieving state-of-the-art zero-shot performance on R2R and REVERIE simultaneously (~10% and ~12% improvements in SR), and showcasing the newly emergent global thinking and path planning abilities of the GPT.
Forward citations
Cited by 5 Pith papers
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Visual-Language-Guided Task Planning for Horticultural Robots
A vision-language model drives a simulated greenhouse robot through simple crop-inspection tasks with ~87% success, but long multi-target tasks collapse to under 10% success.
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A navigation pipeline that bridges object-level global planning with traversability-aware local control, using only RGB images and pretrained models, improves success over prior zero-shot and learned baselines in simulation.
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AeroDuo: Aerial Duo for UAV-based Vision and Language Navigation
Two drones at different altitudes, one guided by a vision-language model and one by a local navigator, reach targets more often than single-drone baselines on a new UAV navigation benchmark.
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DAgger Diffusion Navigation: DAgger Boosted Diffusion Policy for Vision-Language Navigation
A single diffusion policy trained with DAgger, without a waypoint predictor, reports better performance than two-stage waypoint-based models on VLN-CE benchmarks.
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CoNav: Collaborative Cross-Modal Reasoning for Embodied Navigation
CoNav lets a frozen 3D-text model pass spatial text hints to a lightly fine-tuned image-text navigation agent, improving path efficiency on several VLN benchmarks, though not all claimed state-of-the-art results hold.
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