Black box neural networks are an indispensable part of modern robots. Nevertheless, deploying such high-stakes systems in real-world scenarios poses significant challenges when the stakeholders, such as engineers and legislative bodies, lack insights into the neural networks' decision-making process. Presently, explainable AI is primarily tailored to natural language processing and computer vision, falling short in two critical aspects when applied in robots: grounding in decision-making tasks and the ability to assess trustworthiness of their explanations. In this paper, we introduce a trustworthy explainable robotics technique based on human-interpretable, high-level concepts that attribute to the decisions made by the neural network. Our proposed technique provides explanations with associated uncertainty scores by matching neural network's activations with human-interpretable visualizations. To validate our approach, we conducted a series of experiments with various simulated and real-world robot decision-making models, demonstrating the effectiveness of the proposed approach as a post-hoc, human-friendly robot learning diagnostic tool.

Well, I don't know if you are a Batman fan or not, but if you are an AI enthusiastic then this Batman movie script written by AI bot will definitely going to make you crazy. The next movie of Batman is likely to come in 2021 but in the meantime, DC fans are doing what they can to get their Caped Crusader fix. One of such DC fan grab an AI bot with him and watch the old episodes with it. Keaton Patti is the name of this fan and he has created an AI bot whose specialty is writing scripts. Keaton Patti has trained his bot over mover than 1,000 hours of Batman films.