Teaching mobile robots to cooperatively navigate in populated environments

Abstract

Abstract-Mobile service robots are envisioned to operate in environments that are populated by humans and therefore ought to navigate in a socially compliant way. Since the desired behavior of the robots highly depends on the application, we need flexible means for teaching a robot a certain navigation policy. We present an approach that allows a mobile robot to learn how to navigate in the presence of humans while it is being tele-operated in its designated environment. Our method applies feature-based maximum entropy learning to derive a navigation policy from the interactions with the humans. The resulting policy maintains a probability distribution over the trajectories of all the agents that allows the robot to cooperatively avoid collisions with humans. In particular, our method reasons about multiple homotopy classes of the agents' trajectories, i. e., on which sides the agents pass each other. We implemented our approach on a real mobile robot and demonstrate that it is able to successfully navigate in an office environment in the presence of humans relying only on on-board sensors.

Robot navigation in dense human crowds: Statistical models and experimental studies of human–robot cooperation

“…We do call attention to the methods of Kuderer et al (2012), Kretzschmar et al (2013) and Kuderer et al (2013), which leverage the method of maximum entropy (maxEnt) IRL. The advantage to this approach is that one can learn the feature vectors associated with human crowd navigation; for instance, the authors postulate collision avoidance, time to goal, velocity, and acceleration feature vectors, and then train the features using laboratory data on human navigation interactions.…”

Predicting actions to act predictably: Cooperative partial motion planning with maximum entropy models

“…In Henry et al [19], the authors extend maximum entropy IRL to work in partially observable dynamic environments and introduce features that capture aspects of crowd navigation. Kuderer, Kretzschmar et al [1,3,4] leverage this approach to continuous state-spaces and introduce features to capture sociallycompliant navigation behavior. They show that the approach outperforms the social forces model [2] and RVO [8] in terms of its predictive qualities of pedestrian motion.…”

“…As stated in [2], pedestrians tend to dislike other agents entering their "private sphere". By computing the integral over the inverse of the squared Euclidean distance as in [1,3,4], the model prefers pairwise trajectories with larger distance. Fig.…”

“…The work presented in this paper builds upon the approach developed in [1,3,4] in which a maximum entropy distribution over joint navigation behavior is learned from demonstration. The contribution of this paper is the real-world application and evaluation of the approach and modifications to the original formulation to minimize the deployment e↵ort in unknown environments.…”

“…Results from real-world tests are shown in Section V before a conclusion is drawn in Section VI. We highlight deviations from [1,3,4] in the corresponding sections.…”