Recent studies in social robotics show that it can provide economic efficiency and growth in domains such as retail, entertainment, and active and assisted living (AAL). Recent work also highlights that users have the expectation of affordable social robotics platforms, providing focused and specific assistance in a robust manner. In this paper, we present the AMIRO social robotics framework, designed in a modular and robust way for assistive care scenarios. The framework includes robotic services for navigation, person detection and recognition, multi-lingual natural language interaction and dialogue management, as well as activity recognition and general behavior composition. We present AMIRO platform independent implementation based on a Robot Operating System (ROS). We focus on quantitative evaluations of each functionality module, providing discussions on their performance in different settings and the possible improvements. We showcase the deployment of the AMIRO framework on a popular social robotics platform—the Pepper robot—and present the experience of developing a complex user interaction scenario, employing all available functionality modules within AMIRO.

Social assistive robotics is at the forefront of the effort for ensuring independent living and inclusion, especially for the elderly and vulnerable members of the population. We consider that to be proactive is a core requirement of such robotic systems. One key aspect of being proactive is to understand what the user is doing. We explored the task of human action recognition in the context of a social robotic platform based on a framework that we proposed in earlier work. We implemented a processing pipeline specific to this task then collected a dataset using a Pepper robot. The proposed domain adaptation method to increase performance of the method showed promising results.

Currently, the importance of autonomous operating devices is rising with the increasing number of applications that run on robotic platforms or self-driving cars. The context of social robotics assumes that robotic platforms operate autonomously in environments where people perform their daily activities. The ability to re-identify the same people through a sequence of images is a critical component for meaningful human-robot interactions. Considering the quick reactions required by a self-driving car for safety considerations, accurate real-time tracking and people trajectory prediction are mandatory. In this paper, we introduce a real-time people re-identification system based on a trajectory prediction method. We tackled the problem of trajectory prediction by introducing a system that combines semantic information from the environment with social influence from the other participants in the scene in order to predict the motion of each individual. We evaluated the system considering two possible case studies, social robotics and autonomous driving. In the context of social robotics, we integrated the proposed re-identification system as a module into the AMIRO framework that is designed for social robotic applications and assistive care scenarios. We performed multiple experiments in order to evaluate the performance of our proposed method, considering both the trajectory prediction component and the person re-identification system. We assessed the behaviour of our method on existing datasets and on real-time acquired data to obtain a quantitative evaluation of the system and a qualitative analysis. We report an improvement of over 5% for the MOTA metric when comparing our re-identification system with the existing module, on both evaluation scenarios, social robotics and autonomous driving.

The field of socially assistive robotics is advancing steadily, finding an increasing number of applications in domains such as health and home care, public servicing, tourism or entertainment. The diversity of robotic units poses a challenge in terms of their programming, since application designers must either use manufacturer code, or build their own suite of behavior modules from ground up, using open-source platforms such as the Robot Operating System (ROS). The trade-off for design flexibility of the latter option is that there is a lack of ROS-based frameworks for building a behavioral life-cycle for assistive robots, or which allow them to be contextually connected to their environments. In this paper, we introduce the foundational work for AMIRO, a ROS-based framework for the development of assistive robotics applications. We present details of the core behavioral models for navigation, person recognition and communication and detail the means for organizing them into coherent complex behaviors. We further discuss the provisioning for context-aware decision making. The core behavior models are analyzed individually from a performance perspective and two very different evaluation scenarios are described showcasing the versatility of the proposed framework.