arXiv AI
Generalizable Multi-Agent Planning from Signal Temporal Logic Specifications via Diffusion
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
arXiv Machine Learning
Oct 19
Multi-Agent Reinforcement Learning-Based UAV Pathfinding for Obstacle Avoidance in Stochastic Environment
The paper proposes a centralized training with decentralized execution framework for multi‑agent UAV pathfinding in stochastic environments, leveraging multi‑agent reinforcement learning enhanced by model predictive control concepts. Agents communicate only with a centralized planner and use a distance‑weighted mean‑field observation to plan feasible paths, while multi‑step value convergence improves training efficiency. Experiments, including ablation and real‑robot studies, demonstrate the method’s effectiveness and generalization.
arXiv AI
Aug 3
Multi-Agent Planning with Spatio-Temporal and Topological Constraints using STL-GO
arXiv:2607. 28679v1 Announce Type: new Abstract: Multi-agent planning problems arise in a variety of engineering applications, such as multi-robot wildfire fighting and unmanned aerial inspection in factories.
arXiv AI
Jun 19
Formal Verification of Learned Multi-Agent Communication Policies via Decision Tree Distillation
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.
Hugging Face Trending Papers
Jun 17
Formal Verification of Learned Multi-Agent Communication Policies via Decision Tree Distillation
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.
arXiv Machine Learning
Oct 3
Training-Free Diffusion Planning with Analytical Local Scores
The paper presents a training-free diffusion-based motion planner that replaces learned global trajectory scores with analytical local scores derived from obstacle, smoothness, velocity, and inter-agent feasibility terms. By reconstructing trajectory scores through local interactions between neighboring waypoints and nearby constraints, the method decomposes the denoising process while preserving the optimization structure of classical trajectory methods. Experiments demonstrate that this approach generates smooth, feasible trajectories for large multi-agent tasks in complex environments quickly, outperforming learning-based and optimization baselines without requiring training data.
arXiv AI
Sep 1
Scale-Plan: Scalable Language-Enabled Task Planning for Heterogeneous Multi-Robot Teams
arXiv:2603.08814v2 Announce Type: replace-cross Abstract: Long-horizon task planning for heterogeneous multi-robot systems is essential for deploying collaborative teams in real-world environments; y...