There is a growing interest in the use of robotics and AI in the nuclear industry, however it is important to ensure these systems are ethically grounded, trustworthy and safe. An emerging technique to address these concerns is the use of explainability. In this paper we present the results of an interview study with nuclear industry experts to explore the use of explainable intelligent systems within the field. We interviewed 16 participants with varying backgrounds of expertise, and presented two potential use cases for evaluation; a navigation scenario and a task scheduling scenario. Through an inductive thematic analysis we identified the aspects of a deployment that experts want to know from explainable systems and we outline how these associate with the folk conceptual theory of explanation, a framework in which people explain behaviours. We established that an intelligent system should explain its reasons for an action, its expectations of itself, changes in the environment that impact decision making, probabilities and the elements within them, safety implications and mitigation strategies, robot health and component failures during decision making in nuclear deployments. We determine that these factors could be explained with cause, reason, and enabling factor explanations.

Public perceptions of Robotics and Artificial Intelligence (RAI) are important in the acceptance, uptake, government regulation and research funding of this technology. Recent research has shown that the public's understanding of RAI can be negative or inaccurate. We believe effective public engagement can help ensure that public opinion is better informed. In this paper, we describe our first iteration of a high throughput in-person public engagement activity. We describe the use of a light touch quiz-format survey instrument to integrate in-the-wild research participation into the engagement, allowing us to probe both the effectiveness of our engagement strategy, and public perceptions of the future roles of robots and humans working in dangerous settings, such as in the off-shore energy sector. We critique our methods and share interesting results into generational differences within the public's view of the future of Robotics and AI in hazardous environments. These findings include that older peoples' views about the future of robots in hazardous environments were not swayed by exposure to our exhibit, while the views of younger people were affected by our exhibit, leading us to consider carefully in future how to more effectively engage with and inform older people.

Robotics for hazardous environments is currently an important area of research, with the ambition of reducing human risk in potentially devastating situations. Here, we are presenting a Modular Control Framework (MCF) for a low-cost robot with limited sensory resources to address this issue. As a proof of concept, we emulate 3 scenarios - (1) adaptive planning for obstruction avoidance (road block), (2) object identification and support-case-based behaviour adjustment (search and rescue) and (3) autonomous navigation through the environment with reporting of structural status (patrol and monitoring). These were implemented and validated using a Cozmo robot in a small-scale Lego environment. We found that our system can reroute in 90%, can help an injured person 80% and report about failing equipment in 80% of all tested cases, where most of the fails were caused by the object detection used. Our MCF is implemented using ROS, making it easy to use and adjust for other robotic platforms.