RUMOR: Reinforcement learning for Understanding a Model of the Real World for Navigation in Dynamic Environments

TL;DR

RUMOR is a deep reinforcement learning-based planner for differential-drive robots that effectively navigates dynamic environments by incorporating robot constraints and realistic simulation, demonstrating robustness and transferability in real-world tests.

Contribution

It introduces a novel DRL planner using a descriptive velocity space model and kinodynamic-aware action space, bridging the gap between simulation and real-world navigation.

Findings

Outperforms state-of-the-art approaches in dynamic scenarios

Shows robustness and transferability in real-world robot deployments

Effectively incorporates robot constraints into the learning process

Abstract

Autonomous navigation in dynamic environments is a complex but essential task for autonomous robots, with recent deep reinforcement learning approaches showing promising results. However, the complexity of the real world makes it infeasible to train agents in every possible scenario configuration. Moreover, existing methods typically overlook factors such as robot kinodynamic constraints, or assume perfect knowledge of the environment. In this work, we present RUMOR, a novel planner for differential-drive robots that uses deep reinforcement learning to navigate in highly dynamic environments. Unlike other end-to-end DRL planners, it uses a descriptive robocentric velocity space model to extract the dynamic environment information, enhancing training effectiveness and scenario interpretation. Additionally, we propose an action space that inherently considers robot kinodynamics and train it in a simulator that reproduces the real world problematic aspects, reducing the gap between the reality and simulation. We extensively compare RUMOR with other state-of-the-art approaches, demonstrating a better performance, and provide a detailed analysis of the results. Finally, we validate RUMOR's performance in real-world settings by deploying it on a ground robot. Our experiments, conducted in crowded scenarios and unseen environments, confirm the algorithm's robustness and transferability.