A Digital Twin Framework for Virtual Visuo-Haptic Teleoperation of Complex-Shaped Optical Microrobots

In robot teleoperation, haptic feedback can be used to help human operators accomplish dexterous manipulation tasks. However, existing haptic feedback methods try to replicate high-fidelity sensory haptics that are felt in real world interactions, which are constrained by the sensing and feedback hardware capability and may lead to higher workload. To addresses these limitations, this work introduces semantic haptics for teleoperation, which uses abstract haptic patterns to convey critical information about robot states. We categorize robot states into "Confirmations" and "Exceptions", implement a modular haptic rendering pipeline in robot simulation, and deliver semantic haptic feedback to operators through pneumatic and vibrotactile wristbands. This simplifies hardware requirements and enables one-to-many mappings between haptic patterns and robot states. Through three evaluation studies, we identify the most effective semantic haptic design for a common pick and place teleoperation task and compare semantic haptics to other teleoperation feedback approaches including sensory haptics and visual feedback. Results suggest that while semantic haptics performs similarly as other feedback in unimanual tasks, it achieves superior performance in bimanual tasks, with reduced task workload, increased situational awareness, and overall preference.

Robotic ophthalmic surgery offers high precision but introduces a "sensory gap" by decoupling the surgeon from their instrument, resulting in a loss of tactile feedback. This paper presents a novel haptic feedback system for subretinal injection tasks leveraging Scene Graphs (SG). The system bridges the sensory gap by analyzing a physically simulated intraoperative Optical Coherence Tomography (iOCT) feed to construct a real-time surgical SG. The SG serves as a semantic abstraction layer for the surgical scene, which is then utilized by a deterministic, rule-based engine to generate state-dependent haptic feedback on a robotic input device. The system was evaluated in a user study (N=16) using an anthropomorphic head phantom and a custom-built surgical robot. Results demonstrate that the SG-driven haptic feedback improved surgical precision, reducing needle alignment error by 14% (p = 0.044) and improving System Usability Scale (SUS) scores by 8% (p = 0.015), while maintaining comparable task completion times. A needle trajectory analysis revealed the emergence of a safer "Align-then-Approach" strategy, in which our haptic negative reinforcement prompted users to fine-tune the tool's trajectory before approaching the retinal target. This work suggests that SGs can effectively serve as the direct computational foundation for real-time, safety-enhancing context-aware haptic feedback in robotic microsurgery.

Magnetically actuated microrobots have been used as wireless, non-contact manipulation tools at microscales, making them promising for minimally invasive applications. However, their control remains challenging due to indirect actuation, limited sensing, and nonlinear magnetic interactions. In this work, we propose Mag-VLA, a vision-language-action (VLA) model for dexterous magnetic microrobot manipulation using two robotic arms with mounted magnets for dynamic magnetic-field construction. Bimanual coordination enables capabilities such as microrobot reorientation that are difficult or infeasible with a single arm, but it also introduces coupled control challenges, as the policy must generate coordinated trajectories for both actuators within a shared workspace. Our framework adapts a Qwen2.5-VL-7B backbone using Low-Rank Adaptation (LoRA) to process visual observations and language instructions for action prediction. To capture task progression, we introduce a motion-aware phase classifier and a phase-conditioned Action Chunking Transformer (ACT) decoder for temporally coherent multi-step control. We further construct a teleoperated magnetic microrobot manipulation dataset covering three task configurations. Ablation studies show that the ACT-based decoder substantially outperforms alternative generative action heads. In real-robot experiments, Mag-VLA achieves a 90% approach success rate across all tasks and transport success rates of 80%, 70%, and 50% as task difficulty increases. These results demonstrate that hierarchical VLA modeling provides a promising framework for magnetic microrobot manipulation.