In the past five years, social robots have been introduced into public spaces, such as museums, airports, commercial malls, banks, company show rooms, hospitals, and retirement homes, to mention a few examples. In addition to classical robotic skills such as navigation, grasping and manipulating objects, i.e. physical interactions, social robots must be able to communicate with people in the most natural way, i.e. cognitive interactions.
What if robots could take on the repetitive tasks involved in receiving the public? There are already forms of artificial intelligence which are capable of interacting with humans. While there are “butler” robots which can provide the weather forecast or give geographical directions, they are not able to execute complex social tasks autonomously, such as escorting users around a building. To be able to carry out such tasks, a social robot must be capable of perceiving and distinguishing signals emitted by different speakers, understanding these signals and identifying that they are addressed to the robot, and then react accordingly. This is a daunting challenge, because it requires numerous perceptive abilities and a capacity for automatic learning in order to execute autonomous decision-making. SPRING's overall objective is to answer this challenge.
But how do we enable a robot to identify from a set of conversations which request is addressed to it; to understand that it is being asked where a person may sit; to look around and find a vacant seat, determine the path to accompany the speaker to their seat while avoiding other patients and staff on the premises, and then perceive the relevance of offering distraction in the form of conversation? There are numerous technological difficulties and hurdles to overcome in order to accomplish this type of complex task. With regard to movement, SPRING opted to implement the reinforcement learning method. In order to determine its speed, approach angle and other parameters of movement, the robot is trained through an artificial intelligence system which calculates the adequacy between optimal action and the action actually undertaken, and attributes “rewards” for successful outcomes. This training phase enables the robot to come across a wide variety of possible cases in full autonomy, without human intervention to correct pathways. Once placed in real conditions, the robot continues to learn and identify the optimal action for each situation. This opens up the possibility of its use in a hospital setting. This is the aim of the second phase of SPRING, which started in 2022 and ended in May 2024: to validate the use of the robot in a hospital and to assess its impact on users and their habits, in addition to its acceptability. Entrusting even simple social tasks to a robot is nevertheless far from innocuous and raises numerous ethical and organisational issues, which are also handled within the project.
