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Claim this profileWorks12 from public data
- ARiADNE: A Reinforcement learning approach using Attention-based Deep Networks for Exploration72
This work proposes ARiADNE, an attention-based neural approach to obtain real-time, non-myopic path planning for autonomous exploration, which is able to learn dependencies at multiple spatial scales between areas of the agent's partial map, and implicitly predict potential gains associated with exploring those areas.
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- Deep Reinforcement Learning-Based Large-Scale Robot Exploration64
This work proposes a deep reinforcement learning (DRL) based reactive planner to solve large-scale Lidar-based autonomous robot exploration problems in 2D action space that relies on ground truth information and a graph rarefaction algorithm, which allows models trained in small-scale environments to scale to large-scale ones.
- MARVEL: Multi-Agent Reinforcement Learning for Constrained Field-of-View Multi-Robot Exploration in Large-Scale Environments25
This work proposes MARVEL, a neural framework that leverages graph attention networks, together with novel frontiers and orientation features fusion technique, to develop a collaborative, decentralized policy using multi-agent reinforcement learning (MARL) for robots with constrained FoV, and introduces a novel information-driven action pruning strategy.
- Spatio-Temporal Attention Network for Persistent Monitoring of Multiple Mobile Targets21
This work introduces an attention-based neural solution to the persistent monitoring problem, where the agent can learn the inter-dependencies between targets, i.e., their spatial and temporal correlations, conditioned on past measurements.
- Full communication memory networks for team-level cooperation learning7
Two reinforcement learning-based multi-agent models, namely FCMNet and FCMTran, that allow agents to simultaneously learn a differentiable communication mechanism that connects all agents as well as a common, cooperative policy conditioned upon received information are proposed.
- ImagiNav: Scalable Embodied Navigation via Generative Visual Prediction and Inverse Dynamics6
ImagiNav, a novel hierarchical paradigm that formulates navigation in visual space, demonstrates strong zero-shot transfer to robot navigation without requiring robot demonstrations, paving the way for generalist robots that learn navigation directly from unlabeled, open-world data.
- COMPASS: Cooperative Multi-Agent Persistent Monitoring Using Spatio-Temporal Attention Network3
This work proposes COMPASS, a multi-agent reinforcement learning (MARL) framework that enables decentralized agents to persistently monitor multiple moving targets efficiently and model the environment as a graph, where nodes represent spatial locations and edges capture topological proximity, allowing agents to reason over structured layouts and revisit informative regions as needed.
- ORION: Option-Regularized Deep Reinforcement Learning for Cooperative Multi-Agent Online Navigation2
This work designs a shared graph encoder that fuses prior map with online perception into a unified representation, providing robust state embeddings under environmental discrepancies, and introduces a dual-stage cooperation strategy that allows agents to assist teammates under map uncertainty, thereby reducing the overall makespan.
- HEADER: Hierarchical Robot Exploration via Attention-Based Deep Reinforcement Learning with Expert-Guided Reward2
This work presents HEADER, an attention-based reinforcement learning approach with hierarchical graphs for efficient exploration in large-scale environments, and introduces a parameter-free privileged reward that significantly improves model performance and produces near-optimal exploration behaviors.
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- NIPVS-FL: a non-interactive publicly verifiable secure federated-learning scheme against malicious servers–
This work explores how to design a non-interactive and publicly verifiable aggregation scheme that guarantees that as long as the two servers are non-colluding, even a malicious server cannot obtain input privacy of client.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-11. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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