Context-Aware Adaptive Shared Control for Magnetically-Driven Bimanual Dexterous Micromanipulation
This addresses the problem of high cognitive demands and collision risks in magnetic micromanipulation for medical or biological applications, representing a domain-specific incremental improvement.
The paper tackles the challenge of dexterous bimanual magnetic micromanipulation in complex environments by proposing Bi-CAST, a context-aware adaptive shared control framework, which reduces collisions by up to 76.6%, improves trajectory smoothness by 25.9%, and lowers workload by 44.4% compared to baselines.
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.