# Physical Simulation for Multi-agent Multi-machine Tending

arXiv: https://arxiv.org/abs/2410.19761 · HTML: https://mlsanity.com/paper/2410.19761/

## 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 (most cited first, 6 of 6)

- [Sim-to-Real Transfer in Deep Reinforcement Learning for Robotics: a Survey](https://mlsanity.com/paper/2009.13303/) (2020)
- [DayDreamer: World Models for Physical Robot Learning](https://mlsanity.com/paper/2206.14176/) (2022)
- [Orbit: A Unified Simulation Framework for Interactive Robot Learning Environments](https://mlsanity.com/paper/2301.04195/) (2023)
- [VMAS: A Vectorized Multi-Agent Simulator for Collective Robot Learning](https://mlsanity.com/paper/2207.03530/) (2022)
- [From the Lab to the Theater: An Unconventional Field Robotics Journey](https://mlsanity.com/paper/2404.07795/) (2024)
- [Learning Multi-agent Multi-machine Tending by Mobile Robots](https://mlsanity.com/paper/2408.16875/) (2024)

## 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.