The Constitutional Controller: Doubt-Calibrated Steering of Compliant Agents
This work addresses safety and reliability issues for autonomous systems like robots in uncertain environments, though it appears incremental as it builds on existing neuro-symbolic methods.
The paper tackles the challenge of ensuring reliable and rule-compliant behavior in autonomous agents in uncertain environments by introducing the Constitutional Controller (CoCo), a neuro-symbolic framework that integrates probabilistic reasoning with deep learning to enhance safety and compliance, demonstrated in a real-world aerial mobility study.
Ensuring reliable and rule-compliant behavior of autonomous agents in uncertain environments remains a fundamental challenge in modern robotics. Our work shows how neuro-symbolic systems, which integrate probabilistic, symbolic white-box reasoning models with deep learning methods, offer a powerful solution to this challenge. This enables the simultaneous consideration of explicit rules and neural models trained on noisy data, combining the strength of structured reasoning with flexible representations. To this end, we introduce the Constitutional Controller (CoCo), a novel framework designed to enhance the safety and reliability of agents by reasoning over deep probabilistic logic programs representing constraints such as those found in shared traffic spaces. Furthermore, we propose the concept of self-doubt, implemented as a probability density conditioned on doubt features such as travel velocity, employed sensors, or health factors. In a real-world aerial mobility study, we demonstrate CoCo's advantages for intelligent autonomous systems to learn appropriate doubts and navigate complex and uncertain environments safely and compliantly.