Learning Multi-agent Multi-machine Tending by Mobile Robots

• URL: http://arxiv.org/abs/2408.16875v3
• Date: Fri, 28 Feb 2025 04:41:20 GMT
• Title: Learning Multi-agent Multi-machine Tending by Mobile Robots
• Authors: Abdalwhab Abdalwhab, Giovanni Beltrame, Samira Ebrahimi Kahou, David St-Onge,
• Abstract summary: We introduce a multi-agent multi-machine tending learning framework by mobile robots based on Multi-agent Reinforcement Learning (MARL) techniques.<n>An attention-based encoding mechanism is developed and integrated into Multi-agent Proximal Policy Optimization (MAPPO) algorithm to boost its performance for machine tending scenarios.<n>Our model (AB-MAPPO) outperformed MAPPO in this new challenging scenario in terms of task success, safety, and resources utilization.
• Score: 13.812524211429444
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Robotics can help address the growing worker shortage challenge of the manufacturing industry. As such, machine tending is a task collaborative robots can tackle that can also highly boost productivity. Nevertheless, existing robotics systems deployed in that sector rely on a fixed single-arm setup, whereas mobile robots can provide more flexibility and scalability. In this work, we introduce a multi-agent multi-machine tending learning framework by mobile robots based on Multi-agent Reinforcement Learning (MARL) techniques with the design of a suitable observation and reward. Moreover, an attention-based encoding mechanism is developed and integrated into Multi-agent Proximal Policy Optimization (MAPPO) algorithm to boost its performance for machine tending scenarios. Our model (AB-MAPPO) outperformed MAPPO in this new challenging scenario in terms of task success, safety, and resources utilization. Furthermore, we provided an extensive ablation study to support our various design decisions.

Related papers

• Multi-Agent Systems for Robotic Autonomy with LLMs [7.113794752528622]
  The framework includes three core agents: Task Analyst, Robot Designer, and Reinforcement Learning Designer.<n>Results demonstrate that the proposed system can design feasible robots with control strategies when appropriate task inputs are provided.
  arXiv  Detail & Related papers  (2025-05-09T03:52:37Z)
• An LLM-enabled Multi-Agent Autonomous Mechatronics Design Framework [49.633199780510864]
  This work proposes a multi-agent autonomous mechatronics design framework, integrating expertise across mechanical design, optimization, electronics, and software engineering. operating primarily through a language-driven workflow, the framework incorporates structured human feedback to ensure robust performance under real-world constraints. A fully functional autonomous vessel was developed with optimized propulsion, cost-effective electronics, and advanced control.
  arXiv  Detail & Related papers  (2025-04-20T16:57:45Z)
• $\textbf{EMOS}$: $\textbf{E}$mbodiment-aware Heterogeneous $\textbf{M}$ulti-robot $\textbf{O}$perating $\textbf{S}$ystem with LLM Agents [33.77674812074215]
  We introduce a novel multi-agent framework designed to enable effective collaboration among heterogeneous robots. We propose a self-prompted approach, where agents comprehend robot URDF files and call robot kinematics tools to generate descriptions of their physics capabilities. The Habitat-MAS benchmark is designed to assess how a multi-agent framework handles tasks that require embodiment-aware reasoning.
  arXiv  Detail & Related papers  (2024-10-30T03:20:01Z)
• Robotic warehousing operations: a learn-then-optimize approach to large-scale neighborhood search [84.39855372157616]
  This paper supports robotic parts-to-picker operations in warehousing by optimizing order-workstation assignments, item-pod assignments and the schedule of order fulfillment at workstations. We solve it via large-scale neighborhood search, with a novel learn-then-optimize approach to subproblem generation. In collaboration with Amazon Robotics, we show that our model and algorithm generate much stronger solutions for practical problems than state-of-the-art approaches.
  arXiv  Detail & Related papers  (2024-08-29T20:22:22Z)
• BMW Agents -- A Framework For Task Automation Through Multi-Agent Collaboration [0.0]
  We focus on designing a flexible agent engineering framework capable of handling complex use case applications across various domains. The proposed framework provides reliability in industrial applications and presents techniques to ensure a scalable, flexible, and collaborative workflow for multiple autonomous agents.
  arXiv  Detail & Related papers  (2024-06-28T16:39:20Z)
• An Interactive Agent Foundation Model [49.77861810045509]
  We propose an Interactive Agent Foundation Model that uses a novel multi-task agent training paradigm for training AI agents. Our training paradigm unifies diverse pre-training strategies, including visual masked auto-encoders, language modeling, and next-action prediction. We demonstrate the performance of our framework across three separate domains -- Robotics, Gaming AI, and Healthcare.
  arXiv  Detail & Related papers  (2024-02-08T18:58:02Z)
• RoboGen: Towards Unleashing Infinite Data for Automated Robot Learning via Generative Simulation [68.70755196744533]
  RoboGen is a generative robotic agent that automatically learns diverse robotic skills at scale via generative simulation. Our work attempts to extract the extensive and versatile knowledge embedded in large-scale models and transfer them to the field of robotics.
  arXiv  Detail & Related papers  (2023-11-02T17:59:21Z)
• Leveraging Hyperbolic Embeddings for Coarse-to-Fine Robot Design [40.01142267374432]
  Multi-cellular robot design aims to create robots comprised of numerous cells that can be efficiently controlled to perform diverse tasks. Previous research has demonstrated the ability to generate robots for various tasks, but these approaches often optimize robots directly in the vast design space. This paper presents a novel coarse-to-fine method for designing multi-cellular robots.
  arXiv  Detail & Related papers  (2023-11-01T11:56:32Z)
• RoboAgent: Generalization and Efficiency in Robot Manipulation via Semantic Augmentations and Action Chunking [54.776890150458385]
  We develop an efficient system for training universal agents capable of multi-task manipulation skills. We are able to train a single agent capable of 12 unique skills, and demonstrate its generalization over 38 tasks. On average, RoboAgent outperforms prior methods by over 40% in unseen situations.
  arXiv  Detail & Related papers  (2023-09-05T03:14:39Z)
• A Capability and Skill Model for Heterogeneous Autonomous Robots [69.50862982117127]
  capability modeling is considered a promising approach to semantically model functions provided by different machines. This contribution investigates how to apply and extend capability models from manufacturing to the field of autonomous robots.
  arXiv  Detail & Related papers  (2022-09-22T10:13:55Z)
• Learning to Coordinate for a Worker-Station Multi-robot System in Planar Coverage Tasks [16.323122275188354]
  We focus on the multi-robot coverage path planning problem in large-scale planar areas with random dynamic interferers. We introduce a worker-station MRS consisting of multiple workers with limited resources for actual work, and one station with enough resources for resource replenishment. We propose an end-to-end decentralized online planning method, which simultaneously solves coverage planning for workers and rendezvous planning for station.
  arXiv  Detail & Related papers  (2022-08-05T05:36:42Z)
• Bayesian Meta-Learning for Few-Shot Policy Adaptation Across Robotic Platforms [60.59764170868101]
  Reinforcement learning methods can achieve significant performance but require a large amount of training data collected on the same robotic platform. We formulate it as a few-shot meta-learning problem where the goal is to find a model that captures the common structure shared across different robotic platforms. We experimentally evaluate our framework on a simulated reaching and a real-robot picking task using 400 simulated robots.
  arXiv  Detail & Related papers  (2021-03-05T14:16:20Z)
• MAMBPO: Sample-efficient multi-robot reinforcement learning using learned world models [4.84279798426797]
  Multi-robot systems can benefit from reinforcement learning (RL) algorithms that learn behaviours in a small number of trials. We present a novel multi-agent model-based RL algorithm: Multi-Agent Model-Based Policy Optimization (MAMBPO)
  arXiv  Detail & Related papers  (2021-03-05T13:37:23Z)

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.