Geometric Deep Learning Enables Local Sensing for Modular Robot Reconfiguration
A recent study has shown that local sensing can effectively help homogeneous pivoting cube modular robots rearrange themselves in two dimensions. The research, which you can find on arXiv (2509.03140), reveals that while the selection process for the cubes is handled globally, each cube relies on a neural network that uses only local information, trained via reinforcement learning. The study also examines how grid symmetries, like rotation and mirroring, affect the neural network’s setup. It turns out that localized versions are still capable of achieving target shapes, and using more global data can speed up the process. This research, published on September 4, 2025, highlights the potential for decentralized control in modular robotics, particularly in swarm robotics and adaptable structures.
Key facts
- Study demonstrates local sensing is sufficient for global reconfiguration of pivoting cube modular robots in 2D.
- Cube selection is globally coordinated, but each cube's control uses only local neighborhood information.
- Neural networks are trained using reinforcement learning.
- Grid symmetries (rotation and mirroring) are incorporated into the network architecture.
- Even most localized versions succeed in reconfiguring to target shapes.
- Reconfiguration speed increases with more global information.
- Near-optimal reconfiguration achieved with nearest neighbor interactions and multiple information passing steps.
- Paper posted on arXiv with ID 2509.03140.
Entities
Institutions
- arXiv