Using AI-Enhanced Social Robots to Improve Children’s Healthcare Experiences

This paper describes a new research project that aims to develop an autonomous and responsive social robot designed to help children cope with painful procedures in hospital emergency departments. While this is an application domain where psychological interventions have been previously demonstrated to be effective at reducing pain and distress using a variety of devices and techniques, in recent years, social robots have been trialled in this area with promising initial results. However, until now, the social robots that have been tested have generally been teleoperated, which has limited their flexibility and robustness, as well as the potential to offer personalized, adaptive procedural support. Using co-design techniques, this project plans to define and validate the necessary robot behaviour together with participant groups that include children, parents and caregivers, and healthcare professionals. Identified behaviours will be deployed on a robot platform, incorporating AI reasoning techniques that will enable the robot to adapt autonomously to the child’s behaviour. The final robot system will be evaluated through a two-site clinical trial. Throughout the project, we will also monitor and analyse the ethical and social implications of robotics and AI in paediatric healthcare

Plan Verbalisation for Robots Acting in Dynamic Environments

Automated planning provides the tools for intelligent be- haviours in robotic platforms deployed in real-world environ- ments. The complexity of these domains requires planning models that support the system’s dynamics. This results in AI planning approaches often generating plans where the reason- ing around the solution remains obscure for the operator/user. This lack of transparency can reduce trust, results in frequent interventions, and ultimately represents a barrier to adopting autonomous systems. Explanations of behaviour in an easy- to-understand manner, such as in natural language, can help the user comprehend the reasoning behind autonomous ac- tions and help build an accurate mental model. This paper presents an approach for a type of explanation, namely plan verbalisation, that considers the properties of the planning model and describes the system behaviour during plan execu- tion, including replanning and plan repair. We use natural lan- guage techniques to support the disambiguation of the robot decision-making process, considering the planning model en- capsulated using the Planning Domain Definition Language (PDDL). The system is evaluated using an Autonomous Un- derwater Vehicle (AUV) inspection use case

Knowledge Engineering and Planning for Social Human-Robot Interaction: A Case Study

The core task of automated planning is goal-directed action selection; this task is not unique to the planning community, but is also relevant to numerous other research areas within AI. One such area is interactive systems, where a fundamental component called the interaction manager selects actions in the context of conversing with humans using natural language. Although this has obvious parallels to automated planning, using a planner to address the interaction management task relies on appropriate engineering of the underlying planning domain and planning problem to capture the necessary dynamics of the world, the agents involved, their actions, and their knowledge. In this chapter, we describe work on using domain-independent automated planning for action section in social human-robot interaction, focusing on work from the JAMES (Joint Action for Multimodal Embodied Social Systems) robot bartender project