Collision-free Navigation of Human-centered Robots via Markov Games

We exploit Markov games as a framework for collision-free navigation of human-centered robots. Unlike the classical methods which formulate robot navigation as a single-agent Markov decision process with a static environment, our framework of Markov games adopts a multi-agent formulation with one primary agent representing the robot and the remaining auxiliary agents form a dynamic or even competing environment. Such a framework allows us to develop a path-following type adversarial training strategy to learn a robust decentralized collision avoidance policy. Through thorough experiments on both simulated and real-world mobile robots, we show that the learnt policy outperforms the state-of-the-art algorithms in both sample complexity and runtime robustness.