mlsanityData to 14 Oct 2026

Shared Voxel-Map-Based Cooperative Indoor UAV Guidance with a Multi-Agent Soft Actor-Critic Controller

arXiv 2607.25728 ↗First posted 28 Jul 2026cs.RO cs.AI cs.LGcited by 0

On the map: World models → World models for navigation

Abstract (arXiv)

This paper presents a cooperative indoor UAV guidance framework that combines a shared voxel-map world model with a multi-agent Soft Actor-Critic (MASAC) controller. Multiple drones fuse 360 LiDAR observations into a common world-frame occupancy map, which is converted into a compact bird's-eye-view (BEV) representation and provided to each agent as an ego-aligned local crop. This integrate-in-world, act-in- ego design enables consistent multi-UAV spatial fusion whilst retaining decentralised continuous control. The policy combines BEV map features, near-field obstacle observations, and compact goal and peer-state information within a centralised-training, decentralised-execution framework. In simulation, the learned controller achieves a 90.3% success rate in corridor navigation, outperforming Astar planning, an artificial potential field controller, and a prior guidance method. To address residual sim-to-real mismatch, the simulation-trained policy is further adapted using offline imitation fine-tuning from real-world data. Real-world experiments in GNSS-denied indoor environments demonstrate stable two-UAV cooperative operation across increasingly chal- lenging obstacle layouts. The results show that shared voxel-map representations provide an effective and scalable spatial substrate for learned cooperative indoor UAV guidance.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in World models for navigation, one of 11 directions in World models. Builds on 14 of its 24 references that are in the map. First posted on arXiv 28 Jul 2026.

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References · 14

  1. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,885
  2. 2017Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environmentscited by 1,558
  3. 2018Soft Actor-Critic Algorithms and Applicationscited by 1,240
  4. 2024Reconciling Reality through Simulation: A Real-to-Sim-to-Real Approach for Robust Manipulationcited by 144
  5. 2024TRANSIC: Sim-to-Real Policy Transfer by Learning from Online Correctioncited by 59
  6. 2025Residual Off-Policy RL for Finetuning Behavior Cloning Policiescited by 55
  7. 2020Value-Decomposition Multi-Agent Actor-Criticscited by 48
  8. 2024Bootstrapping Reinforcement Learning with Imitation for Vision-Based Agile Flightcited by 31
  9. 2022Direct LiDAR-Inertial Odometry: Lightweight LIO with Continuous-Time Motion Correctioncited by 23
  10. 2025Rapidly Adapting Policies to the Real World via Simulation-Guided Fine-Tuningcited by 17
  11. 2022Robust Adversarial Attacks Detection based on Explainable Deep Reinforcement Learning For UAV Guidance and Planningcited by 13
  12. 2025Generalizable Domain Adaptation for Sim-and-Real Policy Co-Trainingcited by 10
2 more
  1. 2023Sim-to-Real Deep Reinforcement Learning based Obstacle Avoidance for UAVs under Measurement Uncertaintycited by 5
  2. 2025Deep Reinforcement Learning based Autonomous Decision-Making for Cooperative UAVs: A Search and Rescue Real World Applicationcited by 3

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