Task-Oriented Sensing and Covert Transmissions for Collaborative Multi-AUV Systems

• URL: http://arxiv.org/abs/2607.13880v1
• Date: Wed, 15 Jul 2026 14:29:25 GMT
• Title: Task-Oriented Sensing and Covert Transmissions for Collaborative Multi-AUV Systems
• Abstract summary: This paper proposes a Sensed Information Value Realization Multi-Agent Reinforcement Learning (SVR-MARL) framework to characterize the utility of information for cooperative tasks.<n>The potential of the proposed framework to improve collaborative task efficiency while reducing unnecessary communication and exposure risks is demonstrated.
• Score: 67.63956000297922
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In underwater covert cooperative missions, autonomous underwater vehicles (AUVs) often cannot rely on active sonar to continuously obtain complete information, since active sensing and frequent communications increase the risk of exposure. As a result, AUVs primarily rely on passive observation, an approach that yields incomplete local perception and limited task efficiency. Although underwater acoustic communications can mitigate this limitation through information sharing, they are simultaneously constrained by long delays, severe interference, low reliability, and the risk of covert exposure. Existing communications-oriented multi-agent reinforcement learning (MARL) studies often model communication as an ideal information flow, whereas traditional communication optimization primarily focuses on link-level performance. However, both are insufficient to characterize the actual contribution of perceptual information to cooperative tasks under realistic conditions of covert physical communications. This paper proposes a Sensed Information Value Realization Multi-Agent Reinforcement Learning (SVR-MARL) framework that leverages practical information to characterize the utility of information for cooperative tasks and learns distributed cooperative policies under realistic communication and covert constraints. Through a case study of covert multi-AUV cooperative localization and tracking, the potential of the proposed framework to improve collaborative task efficiency while reducing unnecessary communication and exposure risks is demonstrated.

Related papers

• Decentralized Gaussian Process Classification and an Application in Subsea Robotics [10.120009076068953]
  We address the challenge of a team of robots building a map of the probability of communication success from one location to another in real-time.<n>The main contribution of this work is a rigorously derived data sharing policy that selects measurements to be shared among AUVs.<n>We experimentally validate our proposed sharing policy using real acoustic communication data collected from teams of Virginia Tech 690 AUVs.
  arXiv  Detail & Related papers  (2025-11-19T15:26:47Z)
• Robust and Efficient Communication in Multi-Agent Reinforcement Learning [18.405707681765453]
  Multi-agent reinforcement learning (MARL) has made significant strides in enabling coordinated behaviors among autonomous agents.<n>Most existing approaches assume that communication is instantaneous, reliable, and has unlimited bandwidth; these conditions are rarely met in real-world deployments.<n>This survey systematically reviews recent advances in robust and efficient communication strategies for MARL under realistic constraints.
  arXiv  Detail & Related papers  (2025-11-14T15:23:11Z)
• Cooperative Target Detection with AUVs: A Dual-Timescale Hierarchical MARDL Approach [59.81681228738068]
  In adversarial environments, achieving efficient collaboration while ensuring covert operations is a key challenge for underwater cooperative missions.<n>We propose a novel dual time-scale Hierarchical Multi-Agent Proximal Policy Optimization framework.<n>We show that the proposed framework achieves rapid convergence, outperforms benchmark algorithms in terms of performance, and maximizes long-term cooperative efficiency while ensuring covert operations.
  arXiv  Detail & Related papers  (2025-09-16T09:31:32Z)
• In-Context Reinforcement Learning via Communicative World Models [49.00028802135605]
  This work formulates ICRL as a two-agent emergent communication problem.<n>It introduces CORAL, a framework that learns a transferable communicative context.<n>Our experiments demonstrate that this approach enables the CA to achieve significant gains in sample efficiency.
  arXiv  Detail & Related papers  (2025-08-08T19:23:23Z)
• CoSDH: Communication-Efficient Collaborative Perception via Supply-Demand Awareness and Intermediate-Late Hybridization [23.958663737034318]
  We propose a novel communication-efficient collaborative perception framework based on supply-demand awareness and intermediate-late hybridization.<n>Experiments on multiple datasets, including both simulated and real-world scenarios, demonstrate that mymethodname achieves state-of-the-art detection accuracy and optimal bandwidth trade-offs.
  arXiv  Detail & Related papers  (2025-03-05T12:02:04Z)
• Semantic Communication for Cooperative Perception using HARQ [51.148203799109304]
  We leverage an importance map to distill critical semantic information, introducing a cooperative perception semantic communication framework. To counter the challenges posed by time-varying multipath fading, our approach incorporates the use of frequency-division multiplexing (OFDM) along with channel estimation and equalization strategies. We introduce a novel semantic error detection method that is integrated with our semantic communication framework in the spirit of hybrid automatic repeated request (HARQ)
  arXiv  Detail & Related papers  (2024-08-29T08:53:26Z)
• From Explicit Communication to Tacit Cooperation:A Novel Paradigm for Cooperative MARL [14.935456456463731]
  We propose a novel paradigm that facilitates a gradual shift from explicit communication to tacit cooperation. In the initial training stage, we promote cooperation by sharing relevant information among agents. We then combine the explicitly communicated information with the reconstructed information to obtain mixed information.
  arXiv  Detail & Related papers  (2023-04-28T06:56:07Z)
• Interruption-Aware Cooperative Perception for V2X Communication-Aided Autonomous Driving [49.42873226593071]
  We propose V2X communication INterruption-aware COoperative Perception (V2X-INCOP) for V2X communication-aided autonomous driving. We use historical cooperation information to recover missing information due to the interruptions and alleviate the impact of the interruption issue. Experiments on three public cooperative perception datasets demonstrate that the proposed method is effective in alleviating the impacts of communication interruption on cooperative perception.
  arXiv  Detail & Related papers  (2023-04-24T04:59:13Z)
• Over-communicate no more: Situated RL agents learn concise communication protocols [78.28898217947467]
  It is unclear how to design artificial agents that can learn to effectively and efficiently communicate with each other. Much research on communication emergence uses reinforcement learning (RL) We explore situated communication in a multi-step task, where the acting agent has to forgo an environmental action to communicate. We find that while all tested pressures can disincentivise over-communication, situated communication does it most effectively and, unlike the cost on effort, does not negatively impact emergence.
  arXiv  Detail & Related papers  (2022-11-02T21:08:14Z)
• PP-MARL: Efficient Privacy-Preserving Multi-Agent Reinforcement Learning for Cooperative Intelligence in Communications [15.955599283219298]
  Multi-agent reinforcement learning (MARL) is a popular approach for achieving cooperative intelligence (CI) in communication problems.<n> Ensuring privacy protection for MARL is a challenging task because of the presence of heterogeneous agents that learn interdependently via sharing information.<n>We propose PP-MARL, an efficient privacy-preserving learning scheme for MARL.
  arXiv  Detail & Related papers  (2022-04-26T04:08:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.