CoRL-MPPI: Enhancing MPPI With Learnable Behaviours For Efficient And Provably-Safe Multi-Robot Collision Avoidance

Jul 14, 2026

Researchers introduce CoRL-MPPI, a method that integrates Cooperative Reinforcement Learning with Model Predictive Path Integral (MPPI) control for decentralized multi-robot collision avoidance. By training a neural network policy to guide MPPI sampling, the approach improves navigation efficiency and safety while maintaining the theoretical guarantees of MPPI. Experimental results show that CoRL-MPPI outperforms both classical and learning-based baselines in dense, dynamic environments.

Why it matters: This work demonstrates a significant advance in scalable, safe, and efficient multi-robot navigation by combining learning-based behaviors with provably-safe control methods.

Full story at: arXiv Multiagent Systems ↗