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Hybrid Offline-Online Reinforcement Learning for Sensorless, High-Precision Force Regulation in Surgical Robotic Grasping
Description
Precise grasp force regulation in tendon-driven surgical instruments is fundamentally limited by nonlinear coupling between motor dynamics, transmission compliance, friction, and distal mechanics. Existing solutions typically rely on distal force sensing or analytical compensation, increasing hardware complexity or degrading performance under dynamic motion. We present a sensorless control framework that combines physics-consistent modeling and hybrid reinforcement learning to achieve high-preci