proroklab

Distilled version of LaGAT: a neuro-guided search for multi-agent pathfinding (MAPF)

Concrete Multi-Agent Path Planning Enabling Kinodynamically Aggressive Maneuvers (npj Robotics 2026)

Partially Observable Process Gym

VMAS is a vectorized differentiable simulator designed for efficient Multi-Agent Reinforcement Learning benchmarking. It is comprised of a vectorized 2D physics engine written in PyTorch and a set of challenging multi-robot scenarios. Additional scenarios can be implemented through a simple and modular interface.

Pairwise is Not Enough: Hypergraph Neural Networks for Multi-Agent Pathfinding (ICLR-2026)

Graph Attention-Guided Search for Dense Multi-Agent Pathfinding (AAAI-26)

Remotely Detectable Robot Policy Watermarking.

Heterogeneous Multi-Robot Reinforcement Learning

UDP receiver for ESP32-based drone control