# RoboBallet: Planning for Multi-Robot Reaching with Graph Neural Networks and Reinforcement Learning

arXiv: https://arxiv.org/abs/2509.05397 · HTML: https://mlsanity.com/paper/2509.05397/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Placed in RL for robot locomotion and manipulation, one of 15 directions in Deep reinforcement learning methods.
- Builds on 11 of its 30 references that are in the map.
- First posted on arXiv 5 Sep 2025.

## Placed in

- Deep reinforcement learning methods / [RL for robot locomotion and manipulation](https://mlsanity.com/deeprl/rl-for-robot-locomotion-and-manipulation/)

## Builds on (most cited first, 11 of 11)

- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [Gaussian Error Linear Units (GELUs)](https://mlsanity.com/paper/1606.08415/) (2016)
- [Addressing Function Approximation Error in Actor-Critic Methods](https://mlsanity.com/paper/1802.09477/) (2018)
- [Relational inductive biases, deep learning, and graph networks](https://mlsanity.com/paper/1806.01261/) (2018)
- [On the Continuity of Rotation Representations in Neural Networks](https://mlsanity.com/paper/1812.07035/) (2018)
- [Hindsight Experience Replay](https://mlsanity.com/paper/1707.01495/) (2017)
- [Long-Horizon Multi-Robot Rearrangement Planning for Construction Assembly](https://mlsanity.com/paper/2106.02489/) (2021)
- [Learning a Decentralized Multi-arm Motion Planner](https://mlsanity.com/paper/2011.02608/) (2020)
- [The Vizier Gaussian Process Bandit Algorithm](https://mlsanity.com/paper/2408.11527/) (2024)
- [Closed-Chain Manipulation of Large Objects by Multi-Arm Robotic Systems](https://mlsanity.com/paper/1612.01650/) (2016)

## Built on by (most cited first, 5 of 5)

- [MA-VLA: Multi-Arm Vision-Language-Action Model for Collaboration and Compositional Generalization](https://mlsanity.com/paper/2608.25864/) (2026)
- [A Design Co-Pilot for Task-Tailored Manipulators](https://mlsanity.com/paper/2509.13077/) (2025)
- [Advances and Innovations in the Multi-Agent Robotic System (MARS) Challenge](https://mlsanity.com/paper/2601.18733/) (2026)
- [Localising under the drape: proprioception in the era of distributed surgical robotic system](https://mlsanity.com/paper/2510.23512/) (2025)
- [From Pixels to Digital Agents: An Empirical Study on the Taxonomy and Technological Trends of Reinforcement Learning Environments](https://mlsanity.com/paper/2603.23964/) (2026)

## Abstract (arXiv)

Modern robotic manufacturing requires collision-free coordination of multiple robots to complete numerous tasks in shared, obstacle-rich workspaces. Although individual tasks may be simple in isolation, automated joint task allocation, scheduling, and motion planning under spatio-temporal constraints remain computationally intractable for classical methods at real-world scales. Existing multi-arm systems deployed in the industry rely on human intuition and experience to design feasible trajectories manually in a labor-intensive process. To address this challenge, we propose a reinforcement learning (RL) framework to achieve automated task and motion planning, tested in an obstacle-rich environment with eight robots performing 40 reaching tasks in a shared workspace, where any robot can perform any task in any order. Our approach builds on a graph neural network (GNN) policy trained via RL on procedurally-generated environments with diverse obstacle layouts, robot configurations, and task distributions. It employs a graph representation of scenes and a graph policy neural network trained through reinforcement learning to generate trajectories of multiple robots, jointly solving the sub-problems of task allocation, scheduling, and motion planning. Trained on large randomly generated task sets in simulation, our policy generalizes zero-shot to unseen settings with varying robot placements, obstacle geometries, and task poses. We further demonstrate that the high-speed capability of our solution enables its use in workcell layout optimization, improving solution times. The speed and scalability of our planner also open the door to new capabilities such as fault-tolerant planning and online perception-based re-planning, where rapid adaptation to dynamic task sets is required.