Towards a Modular Framework for Human-Robot Interaction and Collaboration

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.