Microscale manipulation has advanced substantially in controlled locomotion and targeted transport, yet many biomedical applications require precise and adaptive interaction with biological micro-objects. At these scales, manipulation is realized through three main classes of platforms: embodied microrobots that physically interact as mobile agents, field-mediated systems that generate contactless trapping or manipulation forces, and externally actuated end-effectors that interact through remotely driven physical tools. Unlike macroscale manipulators, these systems function in fluidic, confined, and surface-dominated environments characterized by negligible inertia, dominant interfacial forces, and soft, heterogeneous, and fragile targets. Consequently, classical assumptions of dexterous manipulation, including rigid-body contact, stable grasping, and rich proprioceptive feedback, become difficult to maintain. This review introduces micro-dexterity as a framework for analyzing biological micromanipulation through the coupled roles of embodiment, perception, and control. We examine how classical manipulation primitives, including pushing, reorientation, grasping, and cooperative manipulation, are reformulated at the microscale; compare the architectures that enable them, from contact-based micromanipulators to contactless field-mediated systems and cooperative multi-agent platforms; and review the perception and control strategies required for task execution. We identify the current dexterity gap between laboratory demonstrations and clinically relevant biological manipulation, and outline key challenges for future translation.
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Fig. 1: From macroscale manipulation to micro-dexterity capability space. Upper left: macroscale dexterity with high-DOF embodiment and multimodal sensing. During downscaling, two fundamental shifts emerge: physics inversion from inertia-dominated to adhesion-dominated interactions, and an environment gradient from open substrates to in vivo vasculature (lower left). Right: the multidimensional capability space of manipulation primitives, target objects, and operational modes.
Fig. 2: Overview of embodiments for micro-dexterous manipulation: harnessing diverse physical interactions and scaling laws to engineer embodied intelligence at the microscale. Tethered MEMS actuators: (A) an electrothermally actuated bimorph microgripper and (B) an electrothermal-magnetic actuated shape memory microgripper. Untethered strategies mirroring the macro-evolution from simple tweezers to complex multi-finger enclosing architectures: (C) a two-finger magnetic microgripper for grasp–transport–release and (D) a multi-arm microgripper with programmable 3D magnetization, (E) reconfigurable multifunctional ferrofluid droplet robots. Alternative physics approaches exploiting (F) capillary forces in bubble-based manipulation and (G) rolling entrapment via magnetic lassos. “Virtual Fingers” enabling non-contact dexterity via field traps: (H) collision-free multi-object navigation via OET and (I) multi-DoF manipulation via acoustic traps. (J) Distributed 6D manipulation via multi-agent and (K) reconfigurable magnetic swarms for collective tasks. Panel (A) is adapted from Ref. [ 36 ] , CC BY 4.0; Panel (B) is reprinted with permission from Ref. [ 37 ] , Copyright 2025, Wiley-VCH GmbH; Panel (E) is reprinted from Ref. [ 38 ] , CC BY-NC-ND 4.0; Panel (F) is reprinted from Ref. [ 39 ] , CC BY 4.0; Panels (H), (I), (J), and (K) are adapted from Refs. [ 40 ] , [ 41 ] , [ 42 ] , and [ 43 ] , respectively, all under CC BY 4.0.
Fig. 3: Sensing, perception, and state estimation for micro-dexterous manipulation. (A) Overview of multi-modal sensing modalities, including tethered force sensors, in vitro and in vivo imaging, and field-coupled sensing. (B-C) Computational pipelines for planar localization, tracking, and depth inference. (D) Force inference from observed kinematic responses. (E-F) Volumetric state estimation for deep-tissue navigation via optoacoustic tracking and vascular mapping. (G) Sim-to-real frameworks for training perception models under limited experimental data. Panel (B) is reprinted from Ref. [ 71 ] , CC BY 4.0; Panel (C) is reprinted from Ref. [ 72 ] , CC BY 4.0; Panel (D) is reprinted with permission from Ref. [ 73 ] , Copyright 2017, Elsevier; Panel (E) is reprinted from Ref. [ 74 ] , CC BY 4.0; Panel (F) is reprinted from Ref. [ 33 ] , CC BY-NC-ND 4.0; Panel (G) is adapted from Ref. [ 75 ] .
Fig. 4: Control and learning for micro-dexterous manipulation. Columns show the progression from external field generation to closed-loop control and learned policies, while rows correspond to magnetic (A, D, G), acoustic/ultrasonic (B, E, H), and optical (C, F, I) actuation. (A-C) Representative actuation mechanisms for each field modality. (D-F) Perception-guided feedback control for manipulation in complex environments. (G-I) Learning-based policies that map observations to control actions for adaptive navigation and task execution. Panels (D) and (G) are reprinted from Ref. [ 105 ] , CC BY 4.0; Panels (E) and (H) are reprinted from Ref. [ 106 ] , CC BY 4.0; Panel (F) is reprinted from Ref. [ 107 ] , CC BY-NC 4.0; Panel (I) is reprinted from Ref. [ 93 ] , CC BY-NC 4.0.
Fig. 5: Task-level micro-dexterous capabilities arranged by increasing dexterity complexity. (A-D) Pick-and-place: reversible capture, transport, and release using soft encapsulation and programmable microgrippers. (E-H) In-hand manipulation: reorientation enabled by micromanipulators and field-defined “virtual fingers”. (I-K) Assembly and intervention: micro-assembly, probing and interventional actions, and collective behaviors for cooperative transport and construction. Panel (C) is reprinted from Ref. [ 54 ] , CC BY-NC-ND 4.0; Panel (F) is reprinted from Ref. [ 119 ] , CC BY-NC-ND 4.0; Panel (G) is reprinted from Ref. [ 120 ] , CC BY-NC 4.0; Panel (H) is reprinted from Ref. [ 67 ] , CC BY-NC-ND 4.0; Panel (I) is reprinted from Ref. [ 121 ] , CC BY 4.0; Panel (J) is reprinted from Ref. [ 107 ] , CC BY-NC 4.0; Panel (K) is reprinted from Ref. [ 122 ] , CC BY 4.0.
Fig. 6: Roadmap towards clinically viable micro-dexterity. This chart illustrates the projected evolution of microrobotics to bridge the current “dexterity gap” where four parallel tracks are outlined: (1) establishing standardized benchmarks and metrics; (2) shifting towards embodied intelligence and reconfigurable architectures; (3) closing the feedback loop via wireless sensing and physics-informed inference; and (4) translating system integration from in vitro proofs to compliant in vivo clinical interventions.
Optical tweezers (OT) provide piconewton-scale manipulation for delicate biomedical tasks, where visuo-haptic feedback can improve operator awareness by conveying interaction-force cues and trap-stability information. However, visuo-haptic teleoperation frameworks for complex-shaped optical microrobots remain underdeveloped, particularly in multi-trap manipulation scenarios. This paper presents a digital twin framework for virtual visuo-haptic teleoperation of complex-shaped OT-driven microrobots. The framework integrates a digital twin environment, image-based pose and depth estimation, microrobot motion simulation, and model-based haptic rendering within a Robot Operating System (ROS)-connected bimanual teleoperation system. For force modeling, we combine a Multi-Sphere Distributed Manipulation (MSDM) model with optical-force estimation from the Optical Tweezers Toolbox, enabling simulator-driven visuo-haptic feedback. The framework reproduces representative microrobot motion trends and provides haptic force rendering that is numerically consistent with the fitted optical-force model. In simulated cell-delivery tasks, haptic feedback reduced the standard deviations of the contact-force metric and the microrobot-to-trap-center distance metric by 53.2% and 55.2%, respectively, and improved task success from 30% to 80%. These results demonstrate the framework's effectiveness for evaluating visuo-haptic teleoperation strategies for complex-shaped optical microrobots.
Zongcai Tan, Lan Wei, Dandan Zhang
Department of Bioengineering, Imperial-X AI Initiative, Imperial College London, London, United Kingdom
Fine-grained, bimanual dexterous manipulation remains a foundational challenge in robotics. Traditional teleoperation systems often fail in contact-rich tasks because embodiment gaps hinder accurate kinematic mapping, while tactile and force feedback remain absent. Consequently, data collection efficiency for high-precision tasks remains prohibitively low. To address these limitations, we propose a tactile-driven adaptation strategy designed to enable fine-grained manipulation on top of teleoperation pipelines. Instantiated within our bimanual dexterous framework, DexTeleop-0, this strategy introduces a real-time optimization loop that bridges the embodiment gap by translating coarse human tracking intents into precise, force-compliant robotic commands with tactile sensing. By estimating accurate contact points and leveraging a tactile-enabled fingertip force-sensing profile, the system dynamically computes localized corrections using the operational space Jacobian with respect to joint angle updates. We rigorously evaluate this tactile-driven adaptation strategy across both simulated environments and real-world hardware. Compared with representative baselines, the proposed method consistently achieves higher task success rates and improved execution efficiency in robust grasping, disturbance-resilient manipulation, and complex dexterous tasks.
Haichao Liu, Yuyao Jiang, Hyunsun Park +2
Nanyang Technological University, Singapore · OOJU, USA
Dexterous teleoperation requires precise arm-hand coordination, low-latency feedback, and robust interaction in real-world contact-rich environments. This paper presents a modular bilateral teleoperation framework that integrates operator-side input interfaces with a robot-side dexterous hand and compliant robotic arm in a unified control architecture. The system supports position-based hand retargeting, differential arm control, multi-scale haptic feedback, and shared control for stable manipulation. We validate the framework through a real-world dexterous manipulation task, highlighting coordinated arm-hand control and contact-aware interaction. Beyond feasibility, we identify key design insights related to cross-embodiment mismatch, haptic feedback granularity, and shared control. The proposed platform provides a practical teleoperation system and a foundation for collecting high-quality demonstrations for future learning-from-demonstration research.
Stefano Dalla Gasperina, Dong Ho Kang, Haiyun Zhang +10
The University of Texas at Austin, Austin, TX, USA · Sony Group Corporation, Tokyo, Japan · Meta Reality Labs Research, Redmond, WA, USA