Learning a Group-Aware Policy for Robot Navigation

Human-aware robot navigation promises a range of applications in which mobile robots bring versatile assistance to people in common human environments. While prior research has mostly focused on modeling pedestrians as independent, intentional individuals, people move in groups; consequently, it is imperative for mobile robots to respect human groups when navigating around people. This paper explores learning group-aware navigation policies based on dynamic group formation using deep reinforcement learning. Through simulation experiments, we show that group-aware policies, compared to baseline policies that neglect human groups, achieve greater robot navigation performance (e.g., fewer collisions), minimize violation of social norms and discomfort, and reduce the robot's movement impact on pedestrians. Our results contribute to the development of social navigation and the integration of mobile robots into human environments.Comment: 8 pages, 4 figure

Socially-Compatible Behavior Design of Autonomous Vehicles with Verification on Real Human Data

As more and more autonomous vehicles (AVs) are being deployed on public roads, designing socially compatible behaviors for them is becoming increasingly important. In order to generate safe and efficient actions, AVs need to not only predict the future behaviors of other traffic participants, but also be aware of the uncertainties associated with such behavior prediction. In this paper, we propose an uncertain-aware integrated prediction and planning (UAPP) framework. It allows the AVs to infer the characteristics of other road users online and generate behaviors optimizing not only their own rewards, but also their courtesy to others, and their confidence regarding the prediction uncertainties. We first propose the definitions for courtesy and confidence. Based on that, their influences on the behaviors of AVs in interactive driving scenarios are explored. Moreover, we evaluate the proposed algorithm on naturalistic human driving data by comparing the generated behavior against ground truth. Results show that the online inference can significantly improve the human-likeness of the generated behaviors. Furthermore, we find that human drivers show great courtesy to others, even for those without right-of-way. We also find that such driving preferences vary significantly in different cultures.Comment: Accepted by IEEE Robotics and Automation Letters. January 202

A Review of Social-Aware Navigation Frameworks for Service Robot in Dynamic Human Environments

The emergence of service robot into human daily life in the past years has opened up various challenges including human-robot interaction, joint-goal achievement and machine learning. Social-aware navigation also gains vast research attention in enhancing the social capabilities of service robots. Human motions are stochastic and social conventions are very complex. Sophisticated approaches are needed for a robot to abide to these social rules and perform obstacle avoidance. To maintain the level of social comfort and achieve a given task, the robot navigation is now no longer a search for a shortest collision-free path, but a multi-objective problem that requires a unified social-aware navigation framework. A careful selection of navigation components including global planner, local planner, the prediction model and a suitable robot platform is also required to offer an effective navigation amidst the dynamic human environment. Hence, this review paper aims to offer insights for service robot implementation by highlighting four varieties of navigation frameworks, various navigation components and different robot platforms

Walking ahead: the headed social force model

Human motion models are finding an increasing number of novel applications in many different fields, such as building design, computer graphics and robot motion planning. The Social Force Model is one of the most popular alternatives to describe the motion of pedestrians. By resorting to a physical analogy, individuals are assimilated to point-wise particles subject to social forces which drive their dynamics. Such a model implicitly assumes that humans move isotropically. On the contrary, empirical evidence shows that people do have a preferred direction of motion, walking forward most of the time. Lateral motions are observed only in specific circumstances, such as when navigating in overcrowded environments or avoiding unexpected obstacles. In this paper, the Headed Social Force Model is introduced in order to improve the realism of the trajectories generated by the classical Social Force Model. The key feature of the proposed approach is the inclusion of the pedestrians' heading into the dynamic model used to describe the motion of each individual. The force and torque representing the model inputs are computed as suitable functions of the force terms resulting from the traditional Social Force Model. Moreover, a new force contribution is introduced in order to model the behavior of people walking together as a single group. The proposed model features high versatility, being able to reproduce both the unicycle-like trajectories typical of people moving in open spaces and the point-wise motion patterns occurring in high density scenarios. Extensive numerical simulations show an increased regularity of the resulting trajectories and confirm a general improvement of the model realism

Intelligent strategies for mobile robotics in laboratory automation

In this thesis a new intelligent framework is presented for the mobile robots in laboratory automation, which includes: a new multi-floor indoor navigation method is presented and an intelligent multi-floor path planning is proposed; a new signal filtering method is presented for the robots to forecast their indoor coordinates; a new human feature based strategy is proposed for the robot-human smart collision avoidance; a new robot power forecasting method is proposed to decide a distributed transportation task; a new blind approach is presented for the arm manipulations for the robots

Designing a robot to evaluate group formations

Robots are making their way in environments inhabited by people. Whether in domestic or public crowded environments, robots should take into consideration social norms and behaviors in order to become a social robot. This dissertation focuses on the problem of how to develop a robotic platform in order to validate human-robot interaction experiences in realistic environments. More specifically, we are concerned with social interactions in human-robot groups in public scenarios, where a variety of people can converge. Our final goal is the develop of a social robot based on certain theories of group behavior and the use of space, known as spatial relationships. The intermediate goals are related with the design and development of the experiences in the wild: as minor changes as possible in the scenario, definition of social tasks, gradual development of a robotic platform as transparent as possible from the robotic side. Initially, this research introduces several preliminary studies of human-robot interaction (HRI) with the PAL Robotics’ REEM robot at the CosmoCaixa Science Museum in Barcelona. Based on certain theories about the use of space as a form of social communication or interaction, the task under study with the commercial robot is as a museum guide, both when the group was in motion (\ie when it was being guided) as well as a group in a static place. Moreover, a second HRI study with REEM robot accomplishing the task of a teacher's assistant was carried out to analyze the perception of the robot's social presence and identity. Likewise, the development of a robotic platform, known as MASHI, for the study of HRI is presented. Based on the service to be completed by the robot, improvements in the experimental robotic platform (structure, morphology, head, face, arms) were carried out in continuous cycles following the development of HRI experiences. This structure should be hold as simple as possible in order to make it `transparent' in the social HRI study. Next, the field study of human-robot social interaction with the MASHI robot with the role of exhibition guide in a cultural center is presented. Based on direct observation techniques, a study is made of the different spatial relationships that are generated when a robot interacts with a person or groups of people. Finally, a novel approach to represent the spatial relationships of HRI in a qualitative way is introduced for future experiences. In this concluding study, we analyze different spatial arrangements generated in a social scenario with a robot within the guide role. As a main conclusion, it can be stated that people follow social norms, in the form of spatial relationships, when interacting with a robot that provide a social service in a public space. Children, however, recurrently challenge these social norms, probably because they are constantly learning about the norms that regulate our coexistence. Spatial relationships are clearly reinforced when the role assigned to the robot is more explicit and understood by people. Spatial relationships can be affected by the characteristics of the environment, either by the available space or by the elements arranged in it, as well as by the number of people who inhabit it. Overall, this dissertation points out that the provided service, and its understanding from the user’s side, is more important that the robotic skills of the robotic platform in order to improve user experiences in public environments.Los robots se abren paso en entornos habitados por personas. Ya sea en entornos domésticos o públicos, los robots deben tener en cuenta ciertas normas y comportamientos sociales para convertirse en un robot social. Esta disertación se centra en el problema de cómo desarrollar una plataforma robótica para validar experiencias de interacción humano-robot en entornos realistas. Más específicamente, nos preocupamos por las interacciones sociales en grupos humano-robot en escenarios públicos, donde una gran variedad de personas puede converger. Nuestro objetivo final es el desarrollo de un robot social basado en ciertas teorías de comportamiento grupal y el uso del espacio, conocidas como relaciones espaciales. Los objetivos intermedios están relacionados con el diseño y desarrollo de las experiencias `en la naturaleza': cambios mínimos como sea posible en el escenario, definición de tareas sociales, desarrollo gradual de una plataforma robótica lo más transparente posible desde el lado robótico. Inicialmente, esta investigación presenta varios estudios preliminares de interacción humano-robot (HRI) con el robot REEM de PAL Robotics en el Museo de Ciencias CosmoCaixa de Barcelona. Basado en ciertas teorías sobre el uso del espacio como una forma de comunicación o interacción social, la tarea en este estudio con el robot comercial es como guía de museo, tanto cuando el grupo estaba en movimiento (es decir, cuando estaba siendo guiado) como cuando el grupo estaba en un lugar estático. Además, se llevó a cabo un segundo estudio de HRI con un robot REEM que realizaba la tarea de un asistente de profesor para analizar la percepción de la presencia e identidad social del robot. Asimismo, se presenta el desarrollo de una plataforma robótica, conocida como MASHI, para el estudio de la HRI. En función del servicio que debe completar el robot, las mejoras en la plataforma robótica experimental (estructura, morfología, cabeza, cara, brazos) se llevaron a cabo en ciclos continuos siguiendo el desarrollo de las experiencias de HRI. Esta estructura debe mantenerse lo más simple posible para que sea 'transparente' en el estudio de HRI social. A continuación, se presenta el estudio de campo de la interacción social humano-robot con el robot MASHI con el papel de guía de exposición en un centro cultural. Con base en técnicas de observación directa, se realiza un estudio de las diferentes relaciones espaciales que se generan cuando un robot interactúa con una persona o grupos de personas. Finalmente, se introduce un enfoque novedoso para representar las relaciones espaciales de la HRI de forma cualitativa para las experiencias futuras. En este estudio final, analizamos diferentes arreglos espaciales generados en un escenario social con un robot con el rol de guía. Como conclusión principal, se puede afirmar que las personas siguen normas sociales, en forma de relaciones espaciales, cuando interactúan con un robot que brinda un servicio social en un espacio público. Los niños, sin embargo, desafían recurrentemente estas normas sociales, probablemente porque están aprendiendo constantemente sobre las normas que regulan nuestra convivencia. Las relaciones espaciales se refuerzan claramente cuando el rol asignado al robot es más explícito y entendido por las personas. Las relaciones espaciales pueden verse afectadas por las características del entorno, ya sea por el espacio disponible o por los elementos dispuestos en él, así como por el número de personas que lo habitan. En general, esta disertación señala que el servicio prestado, y su comprensión del lado del usuario, es más importante que las habilidades robóticas de la plataforma robótica con el fin de mejorar las experiencias del usuario en entornos público