Air-Ground Collaborative Vision-and-Language Navigation (AGC-VLN) pairs an unmanned aerial vehicle (UAV) with a global bird’s‑eye view and an unmanned ground vehicle (UGV) with a local first‑person view, creating a shared bird’s‑eye map that enables collaboration. The training‑free baseline decomposes navigation into VLM‑based semantic reasoning and deterministic geometric execution, allowing the UAV to render the UGV’s pose and target markers while the UGV plans a road‑following path using the shared map. In CARLA‑Air’s Town10HD scene, AGC‑VLN achieves a 77.0% joint success rate, a 27.0% improvement over the weaker individual agent and surpasses the strongest single‑agent baseline by 24.0 points.
Air-Ground Collaborative Vision-and-Language Navigation (AGC‑VLN) pairs a UAV with a global bird’s‑eye view and a UGV with a local first‑person view, creating a shared bird’s‑eye map that displays the UGV’s pose and the target location. The UAV localizes the target in its downward view and flies toward it, while the UGV uses the shared map to plan a road‑following path with a frozen VLM and execute it under closed‑loop control. In CARLA‑Air’s Town10HD scene, AGC‑VLN achieves a 77.0% joint success rate, a 27.0% improvement over the weaker individual agent and outperforms the strongest single‑agent baseline by 24.0 points.
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Abstract: Finding the object referred to by language in a partially observed 3D scene is a core capability for embodied agents. Existing approaches either couple...
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