Autonomous Surgical Robotics: Integrating Real-Time HapticFeedback with Deep Learning for Enhanced Precision

Abstract

Autonomous surgical robotics promises to transform operative care by increasing precision, reducing variability, and enabling novel minimally invasive procedures. A critical barrier to safe, effective autonomy is the lack of rich, low-latency tactile awareness and contextual reasoning during manipulation. This article presents a comprehensive, research-ready treatment of integrating real-time haptic feedback with modern deep learning spanning sensing hardware, control architectures, representation learning, decision-making, and safety/regulatory considerations to enhance robotic surgical precision. We synthesize literature across surgical robotics, haptics, and machine learning; propose a modular system architecture that meets real-time constraints; detail candidate deep models for tactile perception and control (including multimodal fusion and reinforcement learning); and discuss experimental methodologies, evaluation metrics, and clinical translation challenges. We conclude with an agenda for research and deployment that addresses robustness, interpretability, data governance, and standards compliance. (Keywords: surgical robotics, haptics, deep learning, tactile sensing, autonomy, real-time control, safety.)