# Learning Multi-Agent Task Assignment and Navigation in the Factory: from Simulation to Real Robots

arXiv: https://arxiv.org/abs/2609.14567 · HTML: https://mlsanity.com/paper/2609.14567/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 32 references that are in the map.
- First posted on arXiv 13 Sep 2026.

## Builds on (most cited first, 9 of 9)

- [Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments](https://mlsanity.com/paper/1706.02275/) (2017)
- [The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Games](https://mlsanity.com/paper/2103.01955/) (2021)
- [Google Research Football: A Novel Reinforcement Learning Environment](https://mlsanity.com/paper/1907.11180/) (2019)
- [Deep Reinforcement Learning for Robotics: A Survey of Real-World Successes](https://mlsanity.com/paper/2408.03539/) (2024)
- [Reinforcement Learning with Random Delays](https://mlsanity.com/paper/2010.02966/) (2020)
- [skrl: Modular and Flexible Library for Reinforcement Learning](https://mlsanity.com/paper/2202.03825/) (2022)
- [Flying on Point Clouds with Reinforcement Learning](https://mlsanity.com/paper/2503.00496/) (2025)
- [NVIDIA Isaac Sim: Enabling Scalable, GPU-Accelerated Simulation for Robotics](https://mlsanity.com/paper/2606.03551/) (2026)
- [Reinforcement Learning for Control Systems with Time Delays: A Comprehensive Survey](https://mlsanity.com/paper/2602.00399/) (2026)

## Abstract (arXiv)

Reinforcement learning (RL) has shown considerable promise for robotic decision-making, yet deploying multi-agent RL (MARL) on physical multi-robot systems in industrial environments remains challenging. This paper investigates the real-world applicability of decentralized MARL for multi-robot multi-machine tending. We propose Feature-fusion Multi-Agent Proximal Policy Optimization (FMAPPO), which fuses 2D LiDAR measurements with task-specific state information to enable safe decentralized multi-robot task assignment and navigation. A complete simulation-to-reality pipeline was developed using high-fidelity robotic simulation and ROS2 and deployed on physical mobile-manipulator platforms operating under realistic real-world conditions, with the robotic arms disabled during the experiments. We further investigate the sensitivity of the learned policy to command update frequency, an important consideration for real-world deployment. Comparative evaluation in simulation demonstrated that FMAPPO significantly outperformed state-of-the-art baselines with a large effect size, achieving improvements of 106% and 21% in parts delivery and 48% and 11% in parts collection over MAPPO and SMAPPO, respectively. FMAPPO also increased machine utilization by 31 and 10 percentage points, respectively, while reducing collisions by 18% and 15% and increasing the safety score by 14 and 6 percentage points compared with MAPPO and SMAPPO, respectively. Furthermore, real-world experiments demonstrated that the learned decentralized policies can coordinate multiple robots to service multiple machines while maintaining safe operation under real-world sensing and control constraints. Videos of the real-world experiment are available online https://anonymouspapers123.github.io/FMAPPO/.