Assured Autonomy with Neuro-Symbolic Perception
This work addresses the need for assured and resilient AI in safety-critical domains like autonomous systems, representing a paradigm shift rather than an incremental improvement.
The paper tackles the problem of unreliable AI in safety-critical cyber-physical systems by proposing a neuro-symbolic paradigm for perception that integrates symbolic reasoning with data-driven models, demonstrating improved scene understanding through joint object detection and scene graph generation in simulations and real-world datasets.
Many state-of-the-art AI models deployed in cyber-physical systems (CPS), while highly accurate, are simply pattern-matchers.~With limited security guarantees, there are concerns for their reliability in safety-critical and contested domains. To advance assured AI, we advocate for a paradigm shift that imbues data-driven perception models with symbolic structure, inspired by a human's ability to reason over low-level features and high-level context. We propose a neuro-symbolic paradigm for perception (NeuSPaPer) and illustrate how joint object detection and scene graph generation (SGG) yields deep scene understanding.~Powered by foundation models for offline knowledge extraction and specialized SGG algorithms for real-time deployment, we design a framework leveraging structured relational graphs that ensures the integrity of situational awareness in autonomy. Using physics-based simulators and real-world datasets, we demonstrate how SGG bridges the gap between low-level sensor perception and high-level reasoning, establishing a foundation for resilient, context-aware AI and advancing trusted autonomy in CPS.