WorldMAP bootstraps reliable trajectory prediction in vision-language navigation by converting world-model-generated futures into structured supervision, cutting ADE by 18% and FDE by 42.1% on Target-Bench while making small VLMs competitive with large ones.
Aligning cyber space with physical world: A comprehensive survey on embodied ai
3 Pith papers cite this work. Polarity classification is still indexing.
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VLAMotor exposes VLA failures via distance-aware uncertainty testing and synthesizes agent-planned repair data to fine-tune models, reporting 49.25% success rate gains in simulation and 57.5% on hardware.
Introduces a hierarchical VLN architecture with asynchronous layers, incremental memory graph, and WTRP-based exploration that improves success and efficiency on resource-constrained robots.
citing papers explorer
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WorldMAP: Bootstrapping Vision-Language Navigation Trajectory Prediction with Generative World Models
WorldMAP bootstraps reliable trajectory prediction in vision-language navigation by converting world-model-generated futures into structured supervision, cutting ADE by 18% and FDE by 42.1% on Target-Bench while making small VLMs competitive with large ones.
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VLAMotor: Test-Guided Enhancement of Vision-Language-Action Models via Agent-BasedData Synthesis
VLAMotor exposes VLA failures via distance-aware uncertainty testing and synthesizes agent-planned repair data to fine-tune models, reporting 49.25% success rate gains in simulation and 57.5% on hardware.
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A Deployable Embodied Vision-Language Navigation System with Hierarchical Cognition and Context-Aware Exploration
Introduces a hierarchical VLN architecture with asynchronous layers, incremental memory graph, and WTRP-based exploration that improves success and efficiency on resource-constrained robots.