Context-Aware Adaptive Shared Control for Magnetically-Driven Bimanual Dexterous Micromanipulation

TL;DR

This paper introduces Bi-CAST, a context-aware shared control system for magnetic micromanipulation that dynamically adjusts control authority and provides force feedback, significantly improving navigation safety, efficiency, and reducing operator workload.

Contribution

The paper presents a novel multimodal, adaptive shared control framework for bimanual magnetic micromanipulation, integrating visual, risk, and historical data with force feedback for improved dexterous navigation.

Findings

Up to 76.6% reduction in collisions

25.9% improvement in trajectory smoothness

44.4% lower workload (NASA-TLX)

Abstract

Magnetically actuated robots provide a promising untethered platform for navigation in confined environments, enabling biological studies and targeted micro-delivery. However, dexterous manipulation in complex structures remains challenging. While single-arm magnetic actuation suffices for simple transport, steering through tortuous or bifurcating channels demands coordinated control of multiple magnetic sources to generate the torques required for precise rotation and directional guidance. Bimanual teleoperation enables such dexterous steering but imposes high cognitive demands, as operators must handle the nonlinear dynamics of magnetic actuation while coordinating two robotic manipulators. To address these limitations, we propose Bi-CAST, a context-aware adaptive shared control framework for bimanual magnetic micromanipulation. A multimodal network fuses spatio-temporal visual features, spatial risk metrics, and historical states to continuously adjust the control authority of each manipulator in real time. In parallel, a bidirectional haptic interface integrates force-based intent recognition with risk-aware guidance, enabling force feedback to provide a continuous channel for dynamic human-machine authority negotiation. We validate the framework through user studies with eight participants performing three navigation tasks of increasing complexity in a vascular phantom. Compared with fixed authority and discrete switching baselines, Bi-CAST achieves up to 76.6% reduction in collisions, 25.9% improvement in trajectory smoothness, and 44.4% lower NASA-TLX workload, while delivering the fastest task completion times.