# Task-Oriented Formation Decision via Reinforcement Learning: Herding an Attacking Swarm

arXiv: https://arxiv.org/abs/2608.09258 · HTML: https://mlsanity.com/paper/2608.09258/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods.
- Builds on 6 of its 48 references that are in the map.
- First posted on arXiv 10 Aug 2026.

## Placed in

- Deep reinforcement learning methods / [RL for robot navigation and UAV control](https://mlsanity.com/deeprl/rl-for-robot-navigation-and-uav-control/)

## Builds on (most cited first, 6 of 6)

- [Robust optimal density control of robotic swarms](https://mlsanity.com/paper/2205.12592/) (2022)
- [Optimal Spatial-Temporal Triangulation for Bearing-Only Cooperative Motion Estimation](https://mlsanity.com/paper/2310.15846/) (2023)
- [A Novel Graph-based Motion Planner of Multi-Mobile Robot Systems with Formation and Obstacle Constraints](https://mlsanity.com/paper/2210.03340/) (2022)
- [Robust and Efficient Trajectory Planning for Formation Flight in Dense Environments](https://mlsanity.com/paper/2210.04048/) (2022)
- [Self-organized Polygon Formation Control based on Distributed Estimation](https://mlsanity.com/paper/2207.14521/) (2022)
- [Mean-Shift Theory and Its Applications in Swarm Robotics: A New Way to Enhance the Efficiency of Multi-Robot Collaboration](https://mlsanity.com/paper/2510.25086/) (2025)

## Abstract (arXiv)

Multi-robot systems can accomplish tasks that are difficult for a single robot by organizing into task-specific formations. Different from existing studies on multi-robot shape formation, we here study the task-oriented formation decision problem, with a focus on the herding task. This task is challenging due to the attackers' superior maneuverability and their unknown strategies. To address these challenges, we propose the following novel results. First, we encode the formation shape using a low-dimensional parameter vector. This parametric representation reformulates the formation decision as a parameter optimization problem, thereby resolving the limited flexibility of predefined shapes. By optimizing these formation parameters, the defenders' maneuverability disadvantage is mitigated through a formation shape that continuously adapts to task requirements. Second, we develop a reinforcement learning-based policy to regulate the formation parameters. Trained offline in simulations covering diverse attacking strategies, the learned policy can effectively handle adversarial unpredictability during online deployment. Comparative simulations against three baselines demonstrate that our method can successfully accomplish challenging herding tasks. Additional scalability simulations further verify its applicability to simulated scenarios involving dozens of robots. We also validate the practical feasibility of our method on a physical robotic platform with 3 attackers and 7 defenders.