Current autonomous machines lack a deep understanding of their environment, their missions, and the unforeseen situations that affect their performance. Although advances have been made in computer vision and machine learning, these capabilities are not sufficient to endow robots with a level of awareness comparable to that of the humans around them or the engineers who designed them. There is a critical lack of system-level awareness, which seriously limits their reliability, performance, and ability to inspire trust. The core of the problem cannot be solved by deep learning alone: what is needed is a cognitive architecture capable of reasoning and generating causal explanations about its own experience, especially in the face of perceptual anomalies.

Unlike approaches that aim for the robot to explain its own planned actions for human benefit (e.g., traceability or accountability), this project focuses on a deeper challenge: discovering new knowledge from explanations of unexpected perceptual experiences while the robot carries out tasks guided by epistemic goals. This process of knowledge generation—comparable to human scientific insight—relies on pre-existing explicit knowledge and an active control architecture. Its purpose is to transform those anomalies into reusable knowledge, strengthening the system's intelligence for future situations.

The central goal of the INSIGHT proposal is to achieve measurable progress toward robots with self-understanding and self-extending capabilities, that is, systems capable of recognizing their own knowledge gaps and generating new content to fill them. These capabilities will enable the robot to interpret perceptual events within a domain model and share those explanations with human interlocutors, enhancing human-robot collaboration from a more solid and cognitively grounded foundation.