# Learning Safe Unlabeled Multi-Robot Planning with Motion Constraints

arXiv: https://arxiv.org/abs/1907.05300 · HTML: https://mlsanity.com/paper/1907.05300/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jul 2019.

## Built on by (most cited first, 4 of 4)

- [Neurosymbolic Transformers for Multi-Agent Communication](https://mlsanity.com/paper/2101.03238/) (2021)
- [Large Scale Distributed Collaborative Unlabeled Motion Planning with Graph Policy Gradients](https://mlsanity.com/paper/2102.06284/) (2021)
- [Safe Deep Reinforcement Learning for Multi-Agent Systems with Continuous Action Spaces](https://mlsanity.com/paper/2108.03952/) (2021)
- [Turn-based Multi-Agent Reinforcement Learning Model Checking](https://mlsanity.com/paper/2501.03187/) (2025)

## Abstract (arXiv)

In this paper, we present a learning approach to goal assignment and trajectory planning for unlabeled robots operating in 2D, obstacle-filled workspaces. More specifically, we tackle the unlabeled multi-robot motion planning problem with motion constraints as a multi-agent reinforcement learning problem with some sparse global reward. In contrast with previous works, which formulate an entirely new hand-crafted optimization cost or trajectory generation algorithm for a different robot dynamic model, our framework is a general approach that is applicable to arbitrary robot models. Further, by using the velocity obstacle, we devise a smooth projection that guarantees collision free trajectories for all robots with respect to their neighbors and obstacles. The efficacy of our algorithm is demonstrated through varied simulations.