# Learning Multi-agent Multi-machine Tending by Mobile Robots

arXiv: https://arxiv.org/abs/2408.16875 · HTML: https://mlsanity.com/paper/2408.16875/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 29 references that are in the map.
- First posted on arXiv 29 Aug 2024.

## Builds on (most cited first, 16 of 16)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments](https://mlsanity.com/paper/1706.02275/) (2017)
- [Learning Latent Dynamics for Planning from Pixels](https://mlsanity.com/paper/1811.04551/) (2018)
- [DayDreamer: World Models for Physical Robot Learning](https://mlsanity.com/paper/2206.14176/) (2022)
- [VMAS: A Vectorized Multi-Agent Simulator for Collective Robot Learning](https://mlsanity.com/paper/2207.03530/) (2022)
- [Evolutionary Population Curriculum for Scaling Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2003.10423/) (2020)
- [Integrated Task Assignment and Path Planning for Capacitated Multi-Agent Pickup and Delivery](https://mlsanity.com/paper/2110.14891/) (2021)
- [Cooperative Multi-Agent Transfer Learning with Level-Adaptive Credit Assignment](https://mlsanity.com/paper/2106.00517/) (2021)
- [MA-Dreamer: Coordination and communication through shared imagination](https://mlsanity.com/paper/2204.04687/) (2022)
- [Heterogeneous Multi-Agent Reinforcement Learning for Zero-Shot Scalable Collaboration](https://mlsanity.com/paper/2404.03869/) (2024)
- [Fully Distributed Multi-Robot Collision Avoidance via Deep Reinforcement Learning for Safe and Efficient Navigation in Complex Scenarios](https://mlsanity.com/paper/1808.03841/) (2018)
- [Reactive Base Control for On-The-Move Mobile Manipulation in Dynamic Environments](https://mlsanity.com/paper/2309.09393/) (2023)
- [Episodic Memory Model for Learning Robotic Manipulation Tasks](https://mlsanity.com/paper/2104.10218/) (2021)
- [Human-Supervised Semi-Autonomous Mobile Manipulators for Safely and Efficiently Executing Machine Tending Tasks](https://mlsanity.com/paper/2010.04899/) (2020)

## Built on by (most cited first, 1 of 1)

- [Physical Simulation for Multi-agent Multi-machine Tending](https://mlsanity.com/paper/2410.19761/) (2024)

## Abstract (arXiv)

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.