Foresighted robot navigation in dynamic indoor environments with cost-efficient hardware necessitates the use of a lightweight yet dependable controller. So inferring the scene dynamics from sensor readings without explicit object tracking is a pivotal aspect of foresighted navigation among pedestrians. In this paper, we introduce a spatiotemporal attention pipeline for enhanced navigation based on 2D lidar sensor readings. This pipeline is complemented by a novel lidar-state representation that emphasizes dynamic obstacles over static ones. Subsequently, the attention mechanism enables selective scene perception across both space and time, resulting in improved overall navigation performance within dynamic scenarios. We thoroughly evaluated the approach in different scenarios and simulators, finding good generalization to unseen environments. The results demonstrate outstanding performance compared to state-of-the-art methods, thereby enabling the seamless deployment of the learned controller on a real robot.

No abstract

For the best human-robot interaction experience, the robot's navigation policy should take into account personal preferences of the user. In this paper, we present a learning framework complemented by a perception pipeline to train a depth vision-based, personalized navigation controller from user demonstrations. Our refined virtual reality interface enables the demonstration of robot navigation trajectories under motion of the user for dynamic interaction scenarios. In a detailed analysis, we evaluate different configurations of the perception pipeline. As the experiments demonstrate, our new pipeline compresses the perceived depth images to a latent state representation and, thus, enables efficient reasoning about the robot's dynamic environment to the learning. We discuss the robot's navigation performance in various virtual scenes by enrolling a variational autoencoder in combination with a motion predictor and demonstrate the first personalized robot navigation controller that solely relies on depth images.

No abstract