Explainable AI (XAI) in robotics focuses on making autonomous systems' decisions and behaviors transparent and understandable to humans. This involves various techniques to provide insights into a robot's internal reasoning, rather than just its final output. For instance, a robot might explain why it chose a particular path through a cluttered environment, perhaps citing safety protocols or obstacle avoidance metrics. XAI mechanisms often include visualizations of its perception data, natural language explanations for actions, or counterfactual scenarios to clarify its decision logic. Such transparency is vital for building trust in human-robot collaboration, debugging unexpected robot behaviors, and ensuring safety in critical applications. Ultimately, XAI transforms opaque robotic actions into justifiable, intelligible processes, bridging the gap between complex AI and human comprehension. More details: https://www.alakart.bg/web.php?restaurant_id=972&site=https://infoguide.com.ua/