🤖 AI Summary
This work addresses the lack of real-time, interpretable ethical oversight in autonomous robots operating in human-robot coexistence and safety-critical scenarios. It proposes the first neuro-symbolic framework for robotic ethical governance, integrating a fine-tuned DistilBERT model to parse ethical intent from natural language instructions, a probabilistic ethical risk field for modeling and uncertainty estimation, and a threshold-based override control mechanism to enable dynamic supervision and intervention in operational decisions. Experimental results demonstrate that the approach achieves stable convergence in simulated robotic arm tasks, effectively identifies ethical risks, and significantly enhances safe decision-making performance with minimal compromise to task efficiency, while also improving system transparency and interpretability.
📝 Abstract
Ethical decision governance has become a critical requirement for autonomous robotic systems operating in human-centered and safety-sensitive environments. This paper presents a real-time neuro-symbolic ethical governor designed to enable risk-aware supervisory control in autonomous robotic manipulation tasks. The proposed framework integrates transformer-based ethical reasoning with a probabilistic ethical risk field formulation and a threshold-based override control mechanism. language-grounded ethical intent inference capability is learned from natural language task descriptions using a fine-tuned DistilBERT model trained on the ETHICS commonsense dataset. A continuous ethical risk metric is subsequently derived from predicted unsafe action probability, confidence uncertainty, and probabilistic variance to support adaptive decision filtering. The effectiveness of the proposed approach is validated through simulated autonomous robot-arm task scenarios involving varying levels of human proximity and operational hazard. Experimental results demonstrate stable model convergence, reliable ethical risk discrimination, and improved safety-aware decision outcomes without significant degradation of task execution efficiency. The proposed neuro-symbolic architecture further provides enhanced interpretability compared with purely data-driven safety filters, enabling transparent ethical reasoning in real-time control loops. The findings suggest that ethical decision governance can be effectively modeled as a dynamic supervisory risk layer for autonomous robotic systems, with potential applicability to broader cyber-physical and assistive robotics domains.