Map-less Navigation: A Single DRL-based Controller for Robots with Varied Dimensions

Abstract

Deep reinforcement learning (DRL) has shown great potential in training control agents for map-less robot navigation. However, the trained agents are generally dependent on the employed robot in training or dimension-specific, which cannot be directly reused by robots with different dimensional configurations. To address this issue, a DRL-based dimension-variable robot navigation method is proposed in this paper. The proposed approach trains a meta-agent with DRL and then transfers the meta-skill to a robot with a different dimensional configuration (named dimension-scaled robot) using a method named dimension-variable skill transfer (DVST). During the training phase, the meta-agent learns to perform self-navigation with the meta-robot in a simulation environment. In the skill-transfer phase, the observations of the dimension-scaled robot are transferred to the meta-agent in a scaled manner, and the control policy generated by the meta-agent is scaled back to the dimension-scaled robot. Simulation and real-world experimental results indicate that robots with different sizes and angular velocity bounds can accomplish navigation tasks in unknown and dynamic environments without any retraining. This work greatly extends the application range of DRL-based navigation methods from the fixed dimensional configuration to varied dimensional configurations.