Uncertainty-Aware Shared Control for Vision-Based Micromanipulation

Abstract

This paper presents an uncertainty-aware shared control and calibration method for micromanipulation using a digital microscope and a tool-mounted, multi-joint robotic arm, integrating real-time human intervention with a visual-motor policy. Our calibration algorithm leverages co-manipulation control to calibrate the hand-eye transformation without requiring knowledge of the kinematics of the microtool mounted on the robot while remaining robust to camera intrinsics errors. Experimental results show that the proposed calibration method achieves a 39.6% improvement in accuracy over established methods. Additionally, our control structure and calibration method reduces the time required to reach single-point targets from 5.74 s (best conventional method) to 1.91 s, and decreases trajectory tracking errors from 392 μm to 40 μm. These findings establish our method as a robust solution for improving reliability in high-precision biomedical micromanipulation.