Advancing MAPF towards the Real World: A Scalable Multi-Agent Realistic Testbed (SMART)

• URL: http://arxiv.org/abs/2503.04798v1
• Date: Mon, 03 Mar 2025 05:26:59 GMT
• Title: Advancing MAPF towards the Real World: A Scalable Multi-Agent Realistic Testbed (SMART)
• Authors: Jingtian Yan, Zhifei Li, William Kang, Yulun Zhang, Stephen Smith, Jiaoyang Li,
• Abstract summary: Scalable Multi-Agent Realistic Testbed (smart) is a realistic and efficient software tool for evaluating Multi-Agent Path Finding (MAPF) algorithms.<n>We use SMART to explore and demonstrate research questions about the execution of MAPF algorithms in real-world scenarios.
• Score: 37.17845947950689
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: We present Scalable Multi-Agent Realistic Testbed (SMART), a realistic and efficient software tool for evaluating Multi-Agent Path Finding (MAPF) algorithms. MAPF focuses on planning collision-free paths for a group of agents. While state-of-the-art MAPF algorithms can plan paths for hundreds of robots in seconds, they often rely on simplified robot models, making their real-world performance unclear. Researchers typically lack access to hundreds of physical robots in laboratory settings to evaluate the algorithms. Meanwhile, industrial professionals who lack expertise in MAPF require an easy-to-use simulator to efficiently test and understand the performance of MAPF algorithms in their specific settings. SMART fills this gap with several advantages: (1) SMART uses a physics-engine-based simulator to create realistic simulation environments, accounting for complex real-world factors such as robot kinodynamics and execution uncertainties, (2) SMART uses an execution monitor framework based on the Action Dependency Graph, facilitating seamless integration with various MAPF algorithms and robot models, and (3) SMART scales to thousands of robots. In addition, we use SMART to explore and demonstrate research questions about the execution of MAPF algorithms in real-world scenarios. The code is publicly available at https://jingtianyan.github.io/publication/2025-smart.

Related papers

• RAILGUN: A Unified Convolutional Policy for Multi-Agent Path Finding Across Different Environments and Tasks [17.17370365888357]
  Multi-Agent Path Finding (MAPF) is crucial for applications ranging from aerial swarms to warehouse automation.<n>We have developed the first centralized learning-based policy for MAPF problem called RAILGUN.<n>By leveraging a CNN-based architecture, RAILGUN can generalize across different maps and handle any number of agents.
  arXiv  Detail & Related papers  (2025-03-04T20:35:20Z)
• Multi-Agent Motion Planning For Differential Drive Robots Through Stationary State Search [5.9176395108304805]
  Multi-Agent Motion Planning (MAMP) finds various applications in fields such as traffic management, airport operations, and warehouse automation.<n>This paper introduces a three-level framework called MASS to address these challenges.<n> MASS combines MAPF-based methods with our proposed stationary state search planner to generate high-quality kinodynamically-feasible plans.
  arXiv  Detail & Related papers  (2024-12-17T22:17:42Z)
• RoboScript: Code Generation for Free-Form Manipulation Tasks across Real and Simulation [77.41969287400977]
  This paper presents textbfRobotScript, a platform for a deployable robot manipulation pipeline powered by code generation. We also present a benchmark for a code generation benchmark for robot manipulation tasks in free-form natural language. We demonstrate the adaptability of our code generation framework across multiple robot embodiments, including the Franka and UR5 robot arms.
  arXiv  Detail & Related papers  (2024-02-22T15:12:00Z)
• Scalable Mechanism Design for Multi-Agent Path Finding [87.40027406028425]
  Multi-Agent Path Finding (MAPF) involves determining paths for multiple agents to travel simultaneously and collision-free through a shared area toward given goal locations. Finding an optimal solution is often computationally infeasible, making the use of approximate, suboptimal algorithms essential. We introduce the problem of scalable mechanism design for MAPF and propose three strategyproof mechanisms, two of which even use approximate MAPF algorithms.
  arXiv  Detail & Related papers  (2024-01-30T14:26:04Z)
• Waymax: An Accelerated, Data-Driven Simulator for Large-Scale Autonomous Driving Research [76.93956925360638]
  Waymax is a new data-driven simulator for autonomous driving in multi-agent scenes. It runs entirely on hardware accelerators such as TPUs/GPUs and supports in-graph simulation for training. We benchmark a suite of popular imitation and reinforcement learning algorithms with ablation studies on different design decisions.
  arXiv  Detail & Related papers  (2023-10-12T20:49:15Z)
• Asynchronous Multi-Agent Reinforcement Learning for Efficient Real-Time Multi-Robot Cooperative Exploration [16.681164058779146]
  We consider the problem of cooperative exploration where multiple robots need to cooperatively explore an unknown region as fast as possible. Existing MARL-based methods adopt action-making steps as the metric for exploration efficiency by assuming all the agents are acting in a fully synchronous manner. We propose an asynchronous MARL solution, Asynchronous Coordination Explorer (ACE), to tackle this real-world challenge.
  arXiv  Detail & Related papers  (2023-01-09T14:53:38Z)
• Graph-Based Multi-Robot Path Finding and Planning [3.4260993997836753]
  Planning collision-free paths for multiple robots is important for real-world multi-robot systems. Recent advances have resulted in MAPF algorithms that can compute collision-free paths for hundreds of robots.
  arXiv  Detail & Related papers  (2022-06-22T18:47:00Z)
• Kimera-Multi: Robust, Distributed, Dense Metric-Semantic SLAM for Multi-Robot Systems [92.26462290867963]
  Kimera-Multi is the first multi-robot system that is robust and capable of identifying and rejecting incorrect inter and intra-robot loop closures. We demonstrate Kimera-Multi in photo-realistic simulations, SLAM benchmarking datasets, and challenging outdoor datasets collected using ground robots.
  arXiv  Detail & Related papers  (2021-06-28T03:56:40Z)
• PlasticineLab: A Soft-Body Manipulation Benchmark with Differentiable Physics [89.81550748680245]
  We introduce a new differentiable physics benchmark called PasticineLab. In each task, the agent uses manipulators to deform the plasticine into the desired configuration. We evaluate several existing reinforcement learning (RL) methods and gradient-based methods on this benchmark.
  arXiv  Detail & Related papers  (2021-04-07T17:59:23Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.