arXiv AI
Semantic Map Sharing and Capability-Aware Coverage Planning for AI-Native 6G Robotic Coordination
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arXiv AI
Sep 23
Heterogeneous Robot Collaboration in Unstructured Environments with Grounded Generative Intelligence
arXiv:2510.26915v2 Announce Type: replace-cross Abstract: While heterogeneous teams have typically been designed for well-specified missions with known semantics, generative intelligence, i.e., large...
arXiv AI
Sep 1
SGE: Semantically-Guided Exploration for Unstructured Environments via Image-Space Waypoint Sampling
arXiv:2608.29315v1 Announce Type: cross Abstract: This work introduces Semantically-Guided Exploration (SGE), a modular exploration framework for ground vehicles that integrates pixel-level semantic...
arXiv AI
Sep 24
Safe Multi-Robot Coordination via VLM-LLM Reasoning and Reachability Analysis
arXiv:2609.27816v1 Announce Type: cross Abstract: Safe coordination in heterogeneous machine-to-machine (M2M) robotic systems is challenging when robots differ in sensing capabilities, environmental...
arXiv AI
Aug 11
From Semantic Grounding to Decision Optimization: A Unified Framework for Long-Horizon UAV Vision-Language Navigation
arXiv:2608. 09564v1 Announce Type: cross Abstract: UAV vision-language navigation (UAV-VLN) focuses on enabling an aerial agent to follow natural-language instructions in open 3D environments from egocentric visual observations.
arXiv AI
Jul 1
MultiUAV-Plat: An LLM-Oriented Platform, Benchmark and Framework for Multi-UAV Collaborative Task Planning
arXiv:2606. 31073v1 Announce Type: new Abstract: Large language models (LLMs) provide a promising interface for high-level robotic task planning, but their use in multi-UAV collaboration remains difficult to evaluate systematically.
arXiv Computer Vision
Sep 11
Diagnosing and Dynamically Filtering Occupancy World Models for Active Mapping
The paper investigates how inaccuracies in pretrained occupancy networks affect active mapping robots that select camera viewpoints to reconstruct unknown 3D scenes. By fixing the planner and varying the occupancy representation—ranging from no completion to ground‑truth occupancy—the authors find that correcting false positives or false negatives alone does not reliably improve coverage, highlighting a disconnect between occupancy accuracy and planning performance. They propose a dynamic filtering strategy that retains predictions in unexplored space while suppressing unsupported occupancy based on online observations, which preliminarily shows it can steer viewpoint selection toward reachable surfaces that would otherwise remain unseen.