AeroWeaver is a new embodied‑agent harness that integrates large language model (LLM) decision making with the executable skills of individual UAVs, enabling distributed, adaptive swarm execution. It connects semantic mission decisions to governed skills, organizes role‑conditioned local agents for coordination, and refines skill selection online using role‑indexed state‑action‑reward experience. Experiments demonstrate that AeroWeaver maintains valid skill execution without a central joint‑action generator and supports reward‑guided, training‑free adaptive learning from accumulated execution experience.
By Jiabin Lou, Yirong Yang, Haopeng Wang, Xuxin Lv, Xinyu Liu, Diyuan Hou, Xuehong Liu, Rongye Shi, Wenjun Wu
Collective intelligence is a collaborative autonomy paradigm in which multiple agents pursue shared objectives through local perception, information exchange, and coordinated action. UAV swarms embody...
arXiv:2606.03963v4 Announce Type: replace-cross
Abstract: Deep reinforcement learning enables autonomous robots to learn complex navigation tasks, but still relies heavily on time consuming manual re...
By Roohan Ahmed Khan, Yasheerah Yaqoot, Amir Atef Habel, Muhammad Ahsan Mustafa, Dzmitry Tsetserukou
The article explores neuro-symbolic agentic AI (NSAAI) as a framework for enhancing the reliability and adaptability of networked low‑altitude UAVs. It outlines NSAAI’s strengths in data efficiency, compositional generalization, continual learning, and zero‑shot transfer, and presents a reference architecture that integrates task management, neuro‑symbolic planning, verification, metacognition, skill execution, and network interaction. A case study of an urban fire‑inspection mission demonstrates how a UAV can coordinate sensing, cloud access, and verified image‑delivery skills under intermittent connectivity, illustrating NSAAI’s potential for reusable skills, evidence‑grounded decision‑making, and adaptive mission execution.
The paper proposes Neuro‑Symbolic Agentic AI (NSAAI) as a framework that blends neural grounding, symbolic reasoning, and closed‑loop interaction to enhance decision‑making for networked low‑altitude UAVs. It outlines NSAAI’s strengths in data efficiency, compositional generalization, continual learning, and zero‑shot transfer, and presents a reference architecture covering task management, planning, verification, skill execution, and network interaction. An urban fire‑inspection simulation demonstrates how a UAV can coordinate sensing, cloud access, and verified image‑delivery skills under intermittent connectivity, illustrating NSAAI’s potential for reusable skills, evidence‑grounded decisions, and adaptive mission execution.
By Yuqi Ping, Tianhao Liang, Nanchi Su, Guangyu Lei, Junwei Wu, Qinyu Zhang, Tingting Zhang
arXiv:2503.13415v2 Announce Type: replace-cross
Abstract: With the rapid development of artificial intelligence, intelligent decision-making techniques have gradually surpassed human levels in variou...
By Weiqiang Jin, Hongyang Du, Shixiang Tang, Biao Zhao, Guang Yang
arXiv:2507.04356v3 Announce Type: replace-cross
Abstract: Research, innovation and practical capital investment have been increasing rapidly toward the realization of autonomous physical agents. This...
By Vyacheslav Kungurtsev, Alessandro Di Frenna, Gustav Sir, Monicah Cherop Naibei, Haozhe Tian, Homayoun Hamedmoghadam, Akhil Anand, Sebastien Gros
arXiv:2606. 03963v2 Announce Type: replace-cross Abstract: Deep reinforcement learning has shown strong potential for enabling autonomous robots to learn complex navigational tasks.
By Roohan Ahmed Khan, Yasheerah Yaqoot, Muhammad Ahsan Mustafa, Dzmitry Tsetserukou
arXiv:2606. 03963v1 Announce Type: cross Abstract: Deep reinforcement learning has shown strong potential for enabling autonomous robots to learn complex navigational tasks.
By Roohan Ahmed Khan, Yasheerah Yaqoot, Muhammad Ahsan Mustafa, Dzmitry Tsetserukou
arXiv:2606. 19632v1 Announce Type: cross Abstract: Multi-agent reinforcement learning (MARL) enables agents to develop coordination strategies through emergent communication, but neural policies lack the formal safety guarantees required for safety-critical robotic deployment in drone swarms and autonomous vehicle fleets.
By Ahmad Farooq, Kamran Iqbal
Multi-agent reinforcement learning (MARL) enables agents to develop coordination strategies through emergent communication, but neural policies lack the formal safety guarantees required for safety-critical robotic deployment in drone swarms and autonomous vehicle fleets. We present the first end-to-end framework for safety verification of learned multi-agent communication policies through policy abstraction: neural policies are distilled into interpretable decision trees, then formally verified, with empirical validation confirming that verified safety properties transfer to original networks.
The paper introduces a hierarchical hybrid architecture combining large language models (LLMs) and multi-agent reinforcement learning (MARL) to manage heterogeneous unmanned aerial systems in low‑altitude wireless networks (LAWNs). An outer LLM‑driven loop interprets service requirements and operator intent to reconfigure objectives and resource priorities, while an inner MARL loop executes decentralized policies under the updated game. A logistics‑monitoring case study demonstrates the framework’s ability to coordinate diverse services and adapt to changing conditions without retraining the MARL policies.
By Nguyen Duc Minh Quang, Chang Liu, Shuangyang Li, Derrick Wing Kwan Ng