Tootfinder

NeHMO: Neural Hamilton-Jacobi Reachability Learning for Decentralized Safe Multi-Agent Motion Planning
Qingyi Chen, Ahmed H. Qureshi
https://arxiv.org/abs/2507.13940

NeHMO: Neural Hamilton-Jacobi Reachability Learning for Decentralized Safe Multi-Agent Motion Planning
Safe Multi-Agent Motion Planning (MAMP) is a significant challenge in robotics. Despite substantial advancements, existing methods often face a dilemma. Decentralized algorithms typically rely on predicting the behavior of other agents, sharing contracts, or maintaining communication for safety, while centralized approaches struggle with scalability and real-time decision-making. To address these challenges, we introduce Neural Hamilton-Jacobi Reachability Learning (HJR) for Decentralized Multi…

MAPF-World: Action World Model for Multi-Agent Path Finding
Zhanjiang Yang, Meng Li, Yang Shen, Yueming Li, Lijun Sun
https://arxiv.org/abs/2508.12087 https://

MAPF-World: Action World Model for Multi-Agent Path Finding
Multi-agent path finding (MAPF) is the problem of planning conflict-free paths from the designated start locations to goal positions for multiple agents. It underlies a variety of real-world tasks, including multi-robot coordination, robot-assisted logistics, and social navigation. Recent decentralized learnable solvers have shown great promise for large-scale MAPF, especially when leveraging foundation models and large datasets. However, these agents are reactive policy models and exhibit limi…

Willchain: Decentralized, Privacy-Preserving, Self-Executing, Digital Wills
Jovonni L. PHarr
https://arxiv.org/abs/2507.03694 https://

Willchain: Decentralized, Privacy-Preserving, Self-Executing, Digital Wills
This work presents a novel decentralized protocol for digital estate planning that integrates advances distributed computing, and cryptography. The original proof-of-concept was constructed using purely solidity contracts. Since then, we have enhanced the implementation into a layer-1 protocol that uses modern interchain communication to connect several heterogeneous chain types. A key contribution of this research is the implementation of several modern cryptographic primitives to support vari…

Forecast-Driven MPC for Decentralized Multi-Robot Collision Avoidance
Hadush Hailu, Bruk Gebregziabher, Prudhvi Raj
https://arxiv.org/abs/2508.08264 https://

Forecast-Driven MPC for Decentralized Multi-Robot Collision Avoidance
The Iterative Forecast Planner (IFP) is a geometric planning approach that offers lightweight computations, scal- able, and reactive solutions for multi-robot path planning in decentralized, communication-free settings. However, it struggles in symmetric configurations, where mirrored interactions often lead to collisions and deadlocks. We introduce eIFP-MPC, an optimized and extended version of IFP that improves robustness and path consistency in dense, dynamic environments. The method refines…

This https://arxiv.org/abs/2411.13983 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csMA_…

Learning Two-agent Motion Planning Strategies from Generalized Nash Equilibrium for Model Predictive Control
We introduce an Implicit Game-Theoretic MPC (IGT-MPC), a decentralized algorithm for two-agent motion planning that uses a learned value function that predicts the game-theoretic interaction outcomes as the terminal cost-to-go function in a model predictive control (MPC) framework, guiding agents to implicitly account for interactions with other agents and maximize their reward. This approach applies to competitive and cooperative multi-agent motion planning problems which we formulate as const…

Operator-based machine learning framework for generalizable prediction of unsteady treatment dynamics in stormwater infrastructure
Mohamed Shatarah, Kai Liu, Haochen Li
https://arxiv.org/abs/2507.04682

Operator-based machine learning framework for generalizable prediction of unsteady treatment dynamics in stormwater infrastructure
Stormwater infrastructures are decentralized urban water-management systems that face highly unsteady hydraulic and pollutant loadings from episodic rainfall-runoff events. Accurately evaluating their in-situ treatment performance is essential for cost-effective design and planning. Traditional lumped dynamic models (e.g., continuously stirred tank reactor, CSTR) are computationally efficient but oversimplify transport and reaction processes, limiting predictive accuracy and insight. Computatio…

Advancing Learnable Multi-Agent Pathfinding Solvers with Active Fine-Tuning
Anton Andreychuk, Konstantin Yakovlev, Aleksandr Panov, Alexey Skrynnik
https://arxiv.org/abs/2506.23793

Advancing Learnable Multi-Agent Pathfinding Solvers with Active Fine-Tuning
Multi-agent pathfinding (MAPF) is a common abstraction of multi-robot trajectory planning problems, where multiple homogeneous robots simultaneously move in the shared environment. While solving MAPF optimally has been proven to be NP-hard, scalable, and efficient, solvers are vital for real-world applications like logistics, search-and-rescue, etc. To this end, decentralized suboptimal MAPF solvers that leverage machine learning have come on stage. Building on the success of the recently intro…