For the most comfortable, human-aware robot navigation, subjective user preferences need to be taken into account. This paper presents a novel reinforcement learning framework to train a personalized navigation controller along with an intuitive virtual reality demonstration interface. The conducted user study provides evidence that our personalized approach significantly outperforms classical approaches with more comfortable human-robot experiences. We achieve these results using only a few demonstration trajectories from non-expert users, who predominantly appreciate the intuitive demonstration setup. As we show in the experiments, the learned controller generalizes well to states not covered in the demonstration data, while still reflecting user preferences during navigation. Finally, we transfer the navigation controller without loss in performance to a real robot.

In this paper, we present a new approach to human-aware robot navigation, which extends our previous proximity-based navigation framework [1] by introducing visibility and predictability as new parameters. We derived these parameters from a user study and incorporated them into a cost function, which models the user’s discomfort with respect to a relative robot position based on proximity, visibility, predictability, and work efficiency. We use this cost function in combination with an A* planner to create a user-preferred robot navigation policy. In comparison to our previous framework, our new cost function results in a 6% increase in social distance compliance, a 6.3% decrease in visibility of the robot as preferred, and an average decrease of orientation changes of 12.6° per meter resulting in better predictability, while maintaining a comparable average path length. We further performed a virtual reality experiment to evaluate the user comfort based on direct human feedback, finding that the participants on average felt comfortable to very comfortable with the resulting robot trajectories from our approach.

Foresighted, human-aware navigation is a prerequisite for service robots acting in indoor environments. In this paper, we present a novel human-aware navigation approach that relies on long-term prediction of human movements. In particular, we consider the problem of finding a path from the robot's current position to the initially unknown navigation goal of a moving user to provide timely assistance there. The navigation strategy has to minimize the robot's arrival time and at the same time comply with the user's comfort during the movement. Our solution predicts the user's navigation goal based on the robot's observations and prior knowledge about typical human transitions between objects. Based on the motion prediction, we then compute a time-dependent cost map that encodes the belief about the user's positions at future time steps. Using this map, we solve the time-dependent shortest path problem to find an efficient path for the robot, which still abides by the rules of human comfort. To identify robot navigation actions that are perceived as uncomfortable by humans, we performed user surveys and defined the corresponding constraints. We thoroughly evaluated our navigation system in simulation as well as in real-world experiments. As the results show, our system outperforms existing approaches in terms of human comfort, while still minimizing arrival times of the robot.

As service robots are entering more and more homes it gets evermore important to find behavior strategies that ensure a harmonic coexistence between those systems and their users. In this paper, we present a novel approach to enable a mobile robot to provide timely assistance to a user moving in its environment, while simultaneously avoiding unnecessary movements as well as interferences with the user. We developed a framework that uses information about the last object interaction to predict possible future movement destinations of the user and infer where they might need assistance based on prior knowledge. Given this prediction, the robot chooses the best position for itself that minimizes the time until assistance can be provided as well as avoids interferences with other activities of the user. We evaluated our approach in comparison to state-of-the-art methods in simulated environments and performed a user study in a virtual reality environment. Our evaluation demonstrates that our approach is able to decrease both the time until assistance is provided and the travel distance of the robot as well as increases the average distance between the user and the robot in comparison to state-of-the-art systems. Additionally, the robot behavior generated by our method is rated as more pleasant by our study participants than comparable literature approaches.