mlsanityData to 14 Oct 2026

Physical Simulation for Multi-agent Multi-machine Tending

arXiv 2410.19761 ↗First posted 11 Oct 2024cs.RO cs.LGcited by 0

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Abstract (arXiv)

The manufacturing sector was recently affected by workforce shortages, a problem that automation and robotics can heavily minimize. Simultaneously, reinforcement learning (RL) offers a promising solution where robots can learn through interaction with the environment. In this work, we leveraged a simplistic robotic system to work with RL with "real" data without having to deploy large expensive robots in a manufacturing setting. A real-world tabletop arena was designed with robots that mimic the agents' behavior in the simulation. Despite the difference in dynamics and machine size, the robots were able to depict the same behavior as in the simulation. In addition, those experiments provided an initial understanding of the real deployment challenges.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 6 of its 12 references that are in the map. First posted on arXiv 11 Oct 2024.

Builds on

6 of 12 references.

References · 6

  1. 2020Sim-to-Real Transfer in Deep Reinforcement Learning for Robotics: a Surveycited by 413
  2. 2022DayDreamer: World Models for Physical Robot Learningcited by 400
  3. 2023Orbit: A Unified Simulation Framework for Interactive Robot Learning Environmentscited by 295
  4. 2022VMAS: A Vectorized Multi-Agent Simulator for Collective Robot Learningcited by 68
  5. 2024From the Lab to the Theater: An Unconventional Field Robotics Journeycited by 1
  6. 2024Learning Multi-agent Multi-machine Tending by Mobile Robotscited by 1

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  1. 2026Learning Multi-Agent Task Assignment and Navigation in the Factory: from Simulation to Real Robots
  2. 2025Hybrid Robot Learning for Automatic Robot Motion Planning in Manufacturing
  3. 2025A Simulation Pipeline to Facilitate Real-World Robotic Reinforcement Learning Applications
  4. 2025SHaRe-RL: Structured, Interactive Reinforcement Learning for Contact-Rich Industrial Assembly Tasks
  5. 2025A Survey of Reinforcement Learning for Optimization in Automation
  6. 2025Multi-Robot Collaboration through Reinforcement Learning and Abstract Simulation
  7. 2026Cooperative-Competitive Team Play of Real-World Craft Robots
  8. 2024Multi-agent Reinforcement Learning for Dynamic Dispatching in Material Handling Systems