arXiv AI
CoAdapt: An LLM-based Framework for Adaptive Collaborative Perception in IIoT Robotic Swarms
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arXiv AI
Sep 24
A Scalable Multi-Robot Framework for Decentralized and Asynchronous Perception-Action-Communication Loops
arXiv:2309.10164v3 Announce Type: replace-cross Abstract: We develop a decentralized Perception-Action-Communication (PAC) system for multi-robot teams that enables them to collaborate in large scale...
arXiv AI
Jul 2
HydraCollab: Adaptive Collaborative-Perception for Distributed Autonomous Systems
arXiv:2607. 00191v1 Announce Type: cross Abstract: Collaborative-perception enables multi-robot systems to enhance situational awareness by sharing perceptual information.
arXiv AI
Jun 2
AI-IoT-Robotics Integration: Survey of Frameworks, Emerging Trends, and the Path Toward Connected Robotics
arXiv:2606. 01015v1 Announce Type: cross Abstract: The convergence of Artificial Intelligence, the Internet of Things, and Robotics is no longer a futuristic vision; it is rapidly becoming the foundation of real-time, intelligent, and context-aware systems.
arXiv AI
Jun 10
Co-GLANCE: Uncertainty-Aware Active Perception for Heterogeneous Robot Teaming
arXiv:2606. 09919v1 Announce Type: cross Abstract: Perceptual uncertainty is a central challenge for heterogeneous robot teams operating in unstructured outdoor environments, where no single viewpoint affords reliable scene understanding.
Hugging Face Trending Papers
Aug 10
Towards Collaborative Joint Perception and Prediction: Framework, Baseline Evaluation, and Deployment Perspectives
Connected Autonomous Vehicles (CAVs) increasingly exploit Vehicle-to-Everything (V2X) communication to exchange multi-source sensor information, enabling advanced Collaborative Perception (CP) capabilities. Extending beyond these capabilities, this work focuses on Collaborative Joint Perception and Prediction (Co-P&P), a paradigm that unifies CP with motion prediction to mitigate two persistent challenges: the accumulation of perception errors and visual occlusions.
arXiv AI
Oct 2
DuoMind: Enabling Distributed Multi-Robot Coordination with Semantic Communication
DuoMind is a distributed hierarchical framework that enables multi-robot coordination by combining vision-language-action models for low‑level execution with vision-language models for high‑level reasoning and inter‑robot communication. Each robot’s orchestrator processes task instructions, local observations, and messages from peers to generate precise action commands and semantic messages for other robots. The authors introduce RoboPoly, a benchmark of long‑horizon manipulation tasks, and show through experiments that DuoMind improves multi‑robot task performance, with ablation studies highlighting the roles of hierarchical orchestration and semantic communication.