Learn to Reconfigure: Self-supervised Solution Operators for Modular Robotic Swarms

Can deep learning enable fast and safe planning of modular robotic swarms across a vast variety of shape reconfigurations? Modular robotic swarms must change shape quickly while maintaining connectivity throughout the motion. These coupled constraints make planning expensive and restrict exploration in reinforcement learning. We propose a self-supervised solution operator that learns across shape pairs by differentiating task and constraint losses through the swarm dynamics, without demonstrations or precomputed optimal solutions. A graph transformer represents individual modules and their evolving interactions, producing one velocity per module per control period. A fixed projection then maps the learned proposal toward the admissible set, and an independent verifier certifies collision avoidance, connectivity, speed limits, and arrival within a stated tolerance.On two planar benchmarks, the pipeline certifies 71.7% of tasks, compared with 0.8% for a transport baseline and none for an admissible-action reinforcement learning baseline. Three-dimensional experiments demonstrate the same pipeline with up to 1,000 modules.