Communication-Free Distributed Multi-Robot Task Allocation under Partial Observations Using Labeled Multi-Bernoulli Filtering

arXiv:2609.39416 · cs.RO, cs.SY, eess.SY · Submitted 2026-09-30 · Read on arXiv

cs.RO, cs.SY, eess.SY

Submitted: 2026-09-30

Updated: 2026-09-30

Code: https://github.com/scjrobertson/multisensor-lmb-filters

Terminology

• Labeled Multi-Bernoulli — 6× in this paper
• Robustness — 6× in this paper · explained in 5 episodes
• Random Finite Set — 4× in this paper
• Kalman Filter — 3× in this paper
• Core — 2× in this paper · explained in 2 episodes
• Benchmark — 1× in this paper · explained in 2 episodes
• Convergence Guarantees — 1× in this paper · explained in 3 episodes
• Cooperation — 1× in this paper · explained in 2 episodes
• Ground Truth — 1× in this paper
• Institute of Advanced Industrial Science and Technology — 1× in this paper
• Probability Hypothesis Density — 1× in this paper
• Pruning — 1× in this paper · explained in 4 episodes

Related papers

• FMT x: An Efficient and Asymptotically Optimal Extension of the Fast Marching Tree for Dynamic Replanning
• MPCFormer: A physics-informed data-driven approach for explainable socially-aware autonomous driving
• RoboLab: A High-Fidelity Simulation Benchmark for Analysis of Task Generalist Policies
• HRDexDB: A 4D Dexterous Grasping Dataset Across Human and Multiple Robot Embodiments
• APT: Action Expert Pretraining Improves Instruction Generalization of Vision-Language-Action Policies
• Fine-tuning is Not Enough: A Parallel Framework for Collaborative Imitation and Reinforcement Learning in End-to-end Autonomous Driving