Role Playing Learning for Socially Concomitant Mobile Robot Navigation

In this paper, we present the Role Playing Learning (RPL) scheme for a mobile robot to navigate socially with its human companion in populated environments. Neural networks (NN) are constructed to parameterize a stochastic policy that directly maps sensory data collected by the robot to its velocity outputs, while respecting a set of social norms. An efficient simulative learning environment is built with maps and pedestrians trajectories collected from a number of real-world crowd data sets. In each learning iteration, a robot equipped with the NN policy is created virtually in the learning environment to play itself as a companied pedestrian and navigate towards a goal in a socially concomitant manner. Thus, we call this process Role Playing Learning, which is formulated under a reinforcement learning (RL) framework. The NN policy is optimized end-to-end using Trust Region Policy Optimization (TRPO), with consideration of the imperfectness of robot's sensor measurements. Simulative and experimental results are provided to demonstrate the efficacy and superiority of our method

Robot social-aware navigation framework to accompany people walking side-by-side

The final publication is available at link.springer.comWe present a novel robot social-aware navigation framework to walk side-by-side with people in crowded urban areas in a safety and natural way. The new system includes the following key issues: to propose a new robot social-aware navigation model to accompany a person; to extend the Social Force Model,Peer ReviewedPostprint (author's final draft

Combining motion planning with social reward sources for collaborative human-robot navigation task design

Across the human history, teamwork is one of the main pillars sustaining civilizations and technology development. In consequence, as the world embraces omatization, human-robot collaboration arises naturally as a cornerstone. This applies to a huge spectrum of tasks, most of them involving navigation. As a result, tackling pure collaborative navigation tasks can be a good first foothold for roboticists in this enterprise. In this thesis, we define a useful framework for knowledge representation in human-robot collaborative navigation tasks and propose a first solution to the human-robot collaborative search task. After validating the model, two derived projects tackling its main weakness are introduced: the compilation of a human search dataset and the implementation of a multi-agent planner for human-robot navigatio

Human aware robot navigation

Abstract. Human aware robot navigation refers to the navigation of a robot in an environment shared with humans in such a way that the humans should feel comfortable, and natural with the presence of the robot. On top of that, the robot navigation should comply with the social norms of the environment. The robot can interact with humans in the environment, such as avoiding them, approaching them, or following them. In this thesis, we specifically focus on the approach behavior of the robot, keeping the other use cases still in mind. Studying and analyzing how humans move around other humans gives us the idea about the kind of navigation behaviors that we expect the robots to exhibit. Most of the previous research does not focus much on understanding such behavioral aspects while approaching people. On top of that, a straightforward mathematical modeling of complex human behaviors is very difficult. So, in this thesis, we proposed an Inverse Reinforcement Learning (IRL) framework based on Guided Cost Learning (GCL) to learn these behaviors from demonstration. After analyzing the CongreG8 dataset, we found that the incoming human tends to make an O-space (circle) with the rest of the group. Also, the approaching velocity slows down when the approaching human gets closer to the group. We utilized these findings in our framework that can learn the optimal reward and policy from the example demonstrations and imitate similar human motion

Social robot navigation in urban dynamic environments

Deploying mobile robots in social environments requires novel navigation algorithms which are capable of providing valid solutions in such challenging scenarios. The main objective of the present dissertation is to develop new robot navigation approaches able to solve in an intelligent way the navigation problem in urban settings while considering at the same time the interactions with pedestrians, similar to what people easily do with little attention. Before studying in depth navigation algorithms, this thesis focuses on prediction algorithms to provide a more detailed model of the scene. Understanding human motion in outdoor and indoor scenarios is an appealing requirement to characterize correctly urban settings. Urban environments consist essentially of static obstacles and people, which are treated as dynamic and highly uncertain obstacles. Accordingly, it is mandatory to calculate people's intentions in order to successfully build a human prediction model that generates the corresponding human trajectories and considers their interactions with the environment, such as other pedestrians, static obstacles or even robots. It is of great interest that service robots can navigate successfully in typical urban environments, which are dynamic and constrained. In addition, people's behavior should not be conditioned by the presence and the maneuvering of robots. To this end, the robot navigation should seek to minimize its impact on the environment, in our case, on people. This thesis proposes new robot navigation methods that contemplate the social interactions taking place in the scene. In order to procure more intelligence to the navigation algorithm, we propose to integrate seamlessly the human motion prediction information into a new robot planning approach. Real experimentation is essential for the validation of the navigation algorithms. As there are real people involved, we must validate the results in real settings since simulation environments have limitations. In this thesis, we have implemented all the prediction and navigation algorithms in our robotic platform and we have provided plenty of evaluations and testings of our algorithms in real settings.Ubicar robots móviles en entornos sociales requiere novedosos algoritmos de navegación que sean capaces de aportar soluciones válidas en éstos exigentes escenarios. El prinicipal objetivo de la presente disertación es el de desarrollar nuevas soluciones para la navegación de robots que sean capaces de resolver, de una manera más inteligente, los problemas de navegación en emplazamientos urbanos, a la vez que se consideran las interacciones con los transeúntes de manera similar a lo que la gente hace fácilmente prestando poca atención. Antes de estudiar en profundidad los algoritmos de navegación, esta tesis se centra en los algoritmos de predicción para proporcionar un modelo más detallado de la escena. Entender el movimiento humando en entornos exteriores e interiores es un requerimiento deseable para caracterizar correctamente emplazamientos urbanos. Los entornos urbanos están consistituídos por muchos objetos dinámicos y altamente impredecibles, la gente. Por lo tanto, es obligatorio calcular las intenciones de la gente para constriur de manera exitosa un modelo de predicción humano que genere las correspondientes trayectorias humanas y considere sus interacciones con el entorno, como otros peatones, obstáculos estáticos o incluso robots. Es de gran interés que los robots de servicios puedan navegar correctamente en entornos típicamente urbanos, que son dinámicos y acotados, además de que el comportamiento de las personas no debería estar condicionado por la presencia y las maniobras de los robots. Con este fin, la navegación de robots debe buscar minimizar su impacto al entorno, en nuestro caso, a la gente. Esta tesis propone nuevos métodos para la navegación de robots que contemplen las interacciones sociales que suceden en la escena. Para proporcionar una navegación más inteligente, proponemos integrar de manera suave el algoritmo de predicción del movimiento humano con un nuevo enfoque de planificación de trayectorias. La experimentación real es esencial para la validación de los algoritmos de navegación. Ya que hay personas reales implicadas, debemos validar los resultados en emplazamientos reales porque el entorno de simulación tiene limitaciones. En esta tesis hemos implementado todos los algoritmos de predicción y de navegación en la plataforma robótica y hemos proporcionado multitud de evaluaciones y pruebas de nuestros algoritmos en entornos reales