Tootfinder

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…

Mastodon, the decentralized, open social network with over 8 million accounts and nearly 700,000 monthly active users,
is planning to introduce a new feature, called “Packs,”
which will make it easier for newcomers to discover curated collections of users to follow.
The idea is similar to a feature Bluesky, another open social platform, introduced last year.
Starter packs are designed to address the “cold start” problem of joining new social networks.
When you …

Mastodon is taking cues from Bluesky with plans for its own starter 'Packs' | TechCrunch
Mastodon is planning to make it easier for newcomers to discover curated collections of users to follow by launching a new starter packs feature.

R3R: Decentralized Multi-Agent Collision Avoidance with Infinite-Horizon Safety
Thomas Marshall Vielmetti, Devansh R. Agrawal, Dimitra Panagou
https://arxiv.org/abs/2510.06436 h…

R3R: Decentralized Multi-Agent Collision Avoidance with Infinite-Horizon Safety
Existing decentralized methods for multi-agent motion planning lack formal, infinite-horizon safety guarantees, especially for communication-constrained systems. We present R3R, to our knowledge the first decentralized and asynchronous framework for multi-agent motion planning under distance-based communication constraints with infinite-horizon safety guarantees for systems of nonlinear agents. R3R's novelty lies in combining our gatekeeper safety framework with a geometric constraint called R-…

Scalable Solution Methods for Dec-POMDPs with Deterministic Dynamics
Yang You, Alex Schutz, Zhikun Li, Bruno Lacerda, Robert Skilton, Nick Hawes
https://arxiv.org/abs/2508.21595

Scalable Solution Methods for Dec-POMDPs with Deterministic Dynamics
Many high-level multi-agent planning problems, including multi-robot navigation and path planning, can be effectively modeled using deterministic actions and observations. In this work, we focus on such domains and introduce the class of Deterministic Decentralized POMDPs (Det-Dec-POMDPs). This is a subclass of Dec-POMDPs characterized by deterministic transitions and observations conditioned on the state and joint actions. We then propose a practical solver called Iterative Deterministic P…

Cooperative Multi-Agent Path Planning for Heterogeneous UAVs in Contested Environments
Grant Stagg, Cameron K. Peterson
https://arxiv.org/abs/2509.02483 https://

Cooperative Multi-Agent Path Planning for Heterogeneous UAVs in Contested Environments
This paper addresses the challenge of navigating unmanned aerial vehicles in contested environments by introducing a cooperative multi-agent framework that increases the likelihood of safe UAV traversal. The approach involves two types of UAVs: low-priority agents that explore and localize threats, and a high-priority agent that navigates safely to its target destination while minimizing the risk of detection by enemy radar systems. The low-priority agents employ a decentralized optimization al…

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…

Correlation-Aware Dual-View Pose and Velocity Estimation for Dynamic Robotic Manipulation
Mahboubeh Zarei, Robin Chhabra, Farrokh Janabi-Sharifi
https://arxiv.org/abs/2510.05536

Correlation-Aware Dual-View Pose and Velocity Estimation for Dynamic Robotic Manipulation
Accurate pose and velocity estimation is essential for effective spatial task planning in robotic manipulators. While centralized sensor fusion has traditionally been used to improve pose estimation accuracy, this paper presents a novel decentralized fusion approach to estimate both pose and velocity. We use dual-view measurements from an eye-in-hand and an eye-to-hand vision sensor configuration mounted on a manipulator to track a target object whose motion is modeled as random walk (stochasti…

Towards Transparent and Incentive-Compatible Collaboration in Decentralized LLM Multi-Agent Systems: A Blockchain-Driven Approach
Minfeng Qi, Tianqing Zhu, Lefeng Zhang, Ningran Li, Wanlei Zhou
https://arxiv.org/abs/2509.16736

Towards Transparent and Incentive-Compatible Collaboration in Decentralized LLM Multi-Agent Systems: A Blockchain-Driven Approach
Large Language Models (LLMs) have enabled the emergence of autonomous agents capable of complex reasoning, planning, and interaction. However, coordinating such agents at scale remains a fundamental challenge, particularly in decentralized environments where communication lacks transparency and agent behavior cannot be shaped through centralized incentives. We propose a blockchain-based framework that enables transparent agent registration, verifiable task allocation, and dynamic reputation tra…

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…