Twisted string actuators (TSAs) have emerged as promising actuators in robotics owing to their compliant, lightweight nature, and high transmission ratios. However, practical utilization of TSAs remains limited due to their restricted stroke length and intrinsically nonlinear transmission ratio (TR). While variable-radius pulleys (VRPs) can mitigate these issues, they exhibit poor scalability as their volume grows disproportionately with the required stroke or compensation TR range. This paper proposes the dual variable-radius drum TSA (DVRD-TSA) and mathematically proves that its architecture offers superior scalability and compactness compared to existing mechanisms as performance demands increase. For a baseline comparison to validate our model, we fabricated a prototype and compared it to a conventional TSA under constant load conditions with identical initial length. The experiment confirmed that our DVRD-TSA delivers a substantially larger linear stroke (210.3 mm, 72.5%) compared to conventional TSA (85.3 mm, 29.4%), while maintaining a comparable peak torque (25.82 Nmm vs. 24.21 Nmm), and successfully tracks its target near-constant transmission ratio (1.475 rad/mm) with low error. This work presents a compact, passive, and scalable solution that overcomes two major drawbacks of TSAs, nonlinear TR and limited stroke, thereby making them a more compelling option for robotic applications.

This letter presents a compact two-degree-of-freedom (DoF) robotic finger with a flexion-selective passive continuously variable transmission (CVT) to achieve a wide force-speed operating range. Inspired by the functional differentiation of the human metacarpophalangeal (MCP) joint, the proposed mechanism realizes DoF-specific transmission differentiation by selectively assigning passive CVT to the flexion-extension DoF while preserving direct transmission for abduction-adduction. For a wide force-speed operating range, a force-responsive passive CVT is embedded in the flexion pathway, while direct transmission is preserved for the abduction-adduction pathway. To selectively realize transmission adaptation within a multi-DoF MCP mechanism, an output-side passive CVT employing a moving-pulley-inspired wire-routing structure is introduced. The resultant force generated by the wire tensions acting on the pulley that passively increases the flexion moment arm and transmission ratio according to the applied load without additional actuators, sensors, or control. Experimental results demonstrate a maximum output-force amplification of 4.19-fold and a mean amplification of 3.6-fold across the tested flexion angles ranging from 15 degrees to 75 degrees through moment-arm adaptation, thereby substantially expanding the achievable force-speed operating range. Furthermore, dexterous ball-rolling experiments verify that passive transmission adaptation can be achieved while preserving abduction-adduction functionality. These results demonstrate a scalable transmission design strategy for compact multi-DoF robotic hands.

This paper presents a single-actuator passive gripper that achieves both stable grasping and continuous bidirectional in-hand rotation through mechanically encoded power transmission logic. Unlike conventional multifunctional grippers that require multiple actuators, sensors, or control-based switching, the proposed gripper transitions between grasping and rotation solely according to the magnitude of the applied input torque. The key enabler of this behavior is a Twisted Underactuated Mechanism (TUM), which generates non-coplanar motions, namely axial contraction and rotation, from a single rotational input while producing identical contraction regardless of rotation direction. A friction generator mechanically defines torque thresholds that govern passive mode switching, enabling stable grasp establishment before autonomously transitioning to in-hand rotation without sensing or active control. Analytical models describing the kinematics, elastic force generation, and torque transmission of the TUM are derived and experimentally validated. The fabricated gripper is evaluated through quantitative experiments on grasp success, friction-based grasp force regulation, and bidirectional rotation performance. System-level demonstrations, including bolt manipulation, object reorientation, and manipulator-integrated tasks driven solely by wrist torque, confirm reliable grasp to rotate transitions in both rotational directions. These results demonstrate that non-coplanar multifunctional manipulation can be realized through mechanical design alone, establishing mechanically encoded power transmission logic as a robust alternative to actuator and control intensive gripper architectures.

Origami techniques have significantly impacted robotics, expanding its capabilities in shape transformation. Pop-up transformations, inspired by pop-up books, offer intriguing applications in robotics fields, including deployable robots. However, designing origami-inspired robots for transitioning to a completely flat state poses unique challenges, particularly in multistate passive actuation. This article introduces the "pop-up catcher," a gripper designed for multistate passive actuation that can be folded flat and actuated passively to grasp the object. To ensure its reliable state transition, we conduct "transition path planning" with the potential energy surface modulation. We demonstrate the pop-up deployment and passive capture of the target object using our flat-foldable catcher comprised of our pop-up gripper and self-locking modular Sarrus origami that can be folded into a profile less than 25 mm thick while capturing objects over 500 mm away.

Autonomous driving has rapidly advanced with diverse sensors, especially Light Detection and Ranging (LiDAR), which provides precise geometry for tasks like simultaneous localization and mapping (SLAM). Recently, the performance of LiDAR-based SLAM has improved through studies leveraging intensity as a complementary cue to depth. However, in urban environments, dynamic objects occlude static scenes, degrading the stability and accuracy of LiDAR-based SLAM. While previous studies have focused mainly on completing occluded depth, they often disregard intensity, assuming it to be less critical or difficult to estimate due to inherent noise. This overlooks the strong complementary relationship between the two modalities, which can be exploited for effective multimodal completion. Furthermore, completing intensity alongside depth enables broader applicability to intensity-aware perception tasks. To address this issue, a Multimodal Mutual-Guidance (M2G) module is proposed for the joint completion of occluded depth and intensity in LiDAR data. M2G is integrated into a deep learning-based network that takes projected range and intensity images as input, enabling progressive cross-modal feature interaction. Leveraging the shared origin of LiDAR depth and intensity, M2G balances noisy intensity and smooth depth via attention and structure-aware guidance. Experimental results demonstrate that the proposed method outperforms existing inpainting and depth completion approaches, validating its effectiveness for LiDAR completion.

Vine robots can navigate deeply into confined environments by extending their bodies through eversion. In many applications, a tip-mounted camera provides visual feedback for robot control. However, as the robot grows, the tip-mounted camera can undergo unintended rolling about the growth axis due to its weight, tether drag, and environmental friction. This rolling motion causes misalignment between the camera orientation and robot body, limiting reliable operation. To address this issue, we propose a fully passive tip stabilization mechanism that maintains camera orientation during growth. An internal wing-shaped structure is inserted between the robot’s three retraction channels, forming a rigid co-rotating unit with the camera assembly and mechanically coupling it to the robot body. This geometric engagement passively suppresses undesired rolling motion without additional sensors, active control, or changes to the growth mechanism. We analyze the geometric and frictional trade-offs of the wing design and experimentally evaluate multiple configurations. The results show that the proposed mechanism effectively suppresses camera roll while preserving smooth eversion. Demonstrations in straight, curved, and narrow paths further validate passive geometric coupling as a practical approach for tip orientation stabilization in vine robots.

Robotic dressing assistance has the potential to improve the quality of life for individuals with limited mobility. Existing solutions predominantly rely on rigid robotic manipulators, which have challenges in handling deformable garments and ensuring safe physical interaction with the human body. Prior robotic dressing methods require excessive operation times, complex control strategies, and constrained user postures, limiting their practicality and adaptability. This paper proposes a novel soft robotic dressing system, the Self-Wearing Adaptive Garment (SWAG), which uses an unfurling and growth mechanism to facilitate autonomous dressing. Unlike traditional approaches,the SWAG conforms to the human body through an unfurling based deployment method, eliminating skin-garment friction and enabling a safer and more efficient dressing process. We present the working principles of the SWAG, introduce its design and fabrication, and demonstrate its performance in dressing assistance. The proposed system demonstrates effective garment application across various garment configurations, presenting a promising alternative to conventional robotic dressing assistance.

Accurate global localization remains a fundamental challenge in autonomous vehicle navigation. Traditional methods typically rely on high-definition (HD) maps generated through prior traverses or utilize auxiliary sensors, such as a global positioning system (GPS). However, the above approaches are often limited by high costs, scalability issues, and decreased reliability where GPS is unavailable. Moreover, prior methods require route-specific sensor calibration and impose modality-specific constraints, which restrict generalization across different sensor types. The proposed framework addresses this limitation by leveraging a shared embedding space, learned via a weight-sharing Vision Transformer (ViT) encoder, that aligns heterogeneous sensor modalities, Light Detection and Ranging (LiDAR) images, and geo-tagged StreetView panoramas. The proposed alignment enables reliable cross-modal retrieval and coarse-level localization without HD-map priors or route-specific calibration. Further, to address the heading inconsistency between query LiDAR and StreetView, an equirectangular perspective-n-point (PnP) solver is proposed to refine the relative pose through patch-level feature correspondences. As a result, the framework achieves coarse 3-degree-of-freedom (DoF) localization from a single LiDAR scan and publicly available StreetView imagery, bridging the gap between place recognition and metric localization. Experiments demonstrate that the proposed method achieves high recall and heading accuracy, offering scalability in urban settings covered by public Street View without reliance on HD maps.

Model-based control of flexible joint robots with position-controlled actuators relies on accurate knowledge of the joint compliance. In practice, precise stiffness models are often unavailable as the properties of physical elastic elements vary with operating conditions and slowly change over time due to wear and aging. To improve model-based control of these systems, we propose an adaptive control approach in this work, which updates an estimate of the uncertain, nonlinear torque-deflection relation of each joint. As opposed to classical adaptive control approaches for non-elastic robots, we rely on an implicit control law and a control-input-dependent regressor matrix to account for the uncertain joint stiffness. We analyze robustness of the approach against errors induced by the motor position controller. Experimental results on a flexible joint with nonlinear stiffness characteristics demonstrate the effectiveness of the proposed approach.

This paper introduces a Unidirectional Virtual Inerter (UVI) as a novel feedback control element in conjunction with a traditional PD controller for high-bandwidth robot motion control. Designed to harness the beneficial properties of a physical inerter within a digital framework, a UVI overcomes the limitations inherent in the physical domain, such as mechanical design complexity, significant weight and size, and maintenance requirements. The research emphasizes the exploitation of the energy dissipation capability that emerges as the inerter transitions from a physical to a discrete setting. Although inerters are traditionally viewed as energy storage devices, their adaptation to the digital domain reveals a promising energy dissipation function. Moreover, by utilizing the features of the VI in a unidirectional manner, this study delves into the UVI's unique advantages in the digital realm, especially its remarkable energy dissipation ability and the dynamic adjustment of gains based on the system's kinetic energy. This innovative approach, designed to enhance the performance of existing derivative controllers, significantly improves system convergence speed and enhances stability by dissipating the system energy. The paper includes a stability proof using a common Lyapunov function and validates the effectiveness of the UVI in enhancing system stability and tracking accuracy through simulations and experiments with a multi-DoF robotic manipulator. The findings particularly underscore the controller's efficacy in regulation and trajectory-tracking tasks.

Flexible endoscopic surgical robots provide enhanced access and maneuverability in complex anatomical environments, addressing limitations encountered by conventional rigid robotic systems. However, achieving precise control of these robots remains challenging due to inherent nonlinear hysteresis originating from friction and tendon slack in tendon-driven mechanisms, significantly impacting surgical accuracy and increasing the cognitive workload on surgeons. To overcome these challenges, this study introduces MonoEndoCal, a deep learning-based autonomous calibration framework designed to estimate bending angles and compensate for nonlinear hysteresis using only monocular endoscopic images, without the need for additional sensors or markers. Leveraging advanced vision models—Depth Anything V2 for dense depth estimation and Segment Anything Model 2 for zero-shot segmentation—MonoEndoCal accurately estimates robot bending angles in real-time. These angle estimations, along with corresponding actuation commands, are employed to derive a precise hysteresis model using differential equations capturing the robot’s nonlinear dynamics. By integrating this hysteresis model with robot kinematics, MonoEndoCal implements an effective feedforward control strategy that enables precise hysteresis compensation and enhanced control accuracy. Experimental validations further demonstrate its robustness across varying robot geometries, input characteristics (both periodic and aperiodic), and blood contamination, highlighting its strong potential for improving control precision.

The Time Domain Passivity Approach (TDPA) was developed to guarantee passivity while avoiding conservative constant controller parametrization. For this sake, the TDPA applies adaptive damping to dissipate excessive energy resulting from communication delay in bilateral teleoperation setups. Despite its advantages such as model-independent system observation and control or modularity, the transparency in TDPA is limited by two major artifacts: position drift and jitter in the force feedback signal. While a large variety of extensions have been proposed tackling the first issue, the latter received less attention since its solution is considerably more challenging. Recently, a concept with prescient energy reflection was proposed for the energy reflection-based TDPA (TDPA-ER) which significantly reduced force jitter in passive environments. Prescient energy reflection is feasible in TDPA-ER because the coupling controller is integrated within the passivity-controlled two-port subsystem. In this work, we demonstrate that prescient energy reflection can be applied to TDPA by leveraging the deflection-domain passivity approach. Experimental results with round-trip delays of up to 800 ms validate the potential and robustness of the proposed method considering metrics on force attenuation, perceived stiffness, as well as jitter properties.

High-definition (HD) maps, particularly those containing lane-level information regarded as ground truth, are crucial for vehicle localization research. Traditionally, constructing HD maps requires highly accurate sensor measurements collection from the target area, followed by manual annotation to assign semantic information. Consequently, HD maps are limited in terms of geographic coverage. To tackle this problem, in this paper, we propose SIO-Mapper, a novel lane-level HD map construction framework that constructs city-scale maps without physical site visits by utilizing satellite images and OpenStreetMap data. One of the key contributions of SIO-Mapper is its ability to extract lane information more accurately by introducing SIO-Net, a novel deep learning network that integrates features from satellite image and OpenStreetMap using both Transformer-based and convolution-based encoders. Furthermore, to overcome challenges in merging lanes over large areas, we introduce a novel lane integration methodology that combines cluster-based and graph-based approaches. This algorithm ensures the seamless aggregation of lane segments with high accuracy and coverage, even in complex road environments. We validated SIO-Net on the Naver Labs Open Dataset, and the lane-integration mapper on both the Naver Labs and NuScenes datasets,demonstrating competitive performance while relying solely on publicly available inputs.

Navigating confined and complex environments, such as pipes, biological tissues, and collapsed debris, has remained a challenge for conventional robotic systems, which often struggle with maneuverability and adaptability. Soft toroidal robots offer a promising alternative, with a compact and lightweight toroidal shape that allows continuous movement without requiring bulky external equipment. However, the lack of a steering mechanism has limited their applicability in dynamic and complex terrains. To overcome this, we developed a steering mechanism that leverages the bistable characteristics inherent in the toroidal structure to enable curvature formation. By adjusting the position of the tail within the structure, the robot can change its direction of bending, enabling flexible and responsive steering. To achieve this bistable behavior, we utilized the orthotropic properties of ripstop nylon fabric, reducing the robot's bending stiffness and enhancing its steering capabilities. Through theoretical modeling and experimental validation, we identified key design parameters, such as optimal operating pressure and steering device length. The proposed soft toroidal robot, with a diameter of 70 mm and a total length of 400 mm, achieves 1-degree of freedom (DOF) steering by exploiting this bistable deformation. Our experiments demonstrated its ability to navigate a T-shaped pipe and climb vertically in confined spaces, achieving a maximum curvature of 13.4 m-1. These findings highlight the potential of soft toroidal robots for maneuvering through both confined and open environments with enhanced adaptability and efficiency.

The time-domain passivity approach (TDPA) is a widely used control strategy for ensuring passivity with a model free architecture. However, the TDPA dissipates all observed active energy in a single step to ensure passivity, leading to sudden force modifications. This, in turn, reduces the transparency and breaks the illusion of reality in haptic interactions. In this paper, we introduce a new concept to improve the TDPA through anticipatory action before the actual active behaviour is observed. Using the gradient of the observed energy, we estimate when the passivity condition is violated and predict the amount of active energy. Based on the prediction horizon, we apply a scaling factor to determine the amount of energy that should be dissipated through adaptive damping. We update the adaptive damping at each sampling period according to the gradient of the observed energy, which changes as the applied damping modifies the system behaviour. This approach enables smoother control by distributing the energy dissipation over time, as opposed to dissipating it in a single step, as is the case with TDPA. Experiments were conducted using a Haply Inverse3 haptic device, and a user study was performed to evaluate the proposed method. The results demonstrate the benefits of the proposed approach compared with conventional TDPA.

This paper introduces the Mechacnially prOgrammed Radius-adjustable PHysical (MORPH) wheel, a fully passive variable-radius wheel that embeds mechanical behavior logic for torque-responsive transformation. Unlike conventional variable transmission systems relying on actuators, sensors, and active control, the MORPH wheel achieves passive adaptation solely through its geometry and compliant structure. The design integrates a torque-response coupler and spring-loaded connecting struts to mechanically adjust the wheel radius between 80 mm and 45 mm in response to input torque, without any electrical components. The MORPH wheel provides three unique capabilities rarely achieved simultaneously in previous passive designs: (1) bidirectional operation with unlimited rotation through a symmetric coupler; (2) high torque capacity exceeding 10 N with rigid power transmission in drive mode; and (3) precise and repeatable transmission ratio control governed by deterministic kinematics. A comprehensive analytical model was developed to describe the wheel's mechanical behavior logic, establishing threshold conditions for mode switching between direct drive and radius transformation. Experimental validation confirmed that the measured torque-radius and force-displacement characteristics closely follow theoretical predictions across wheel weights of 1.8-2.8kg. Robot-level demonstrations on varying loads (0-25kg), slopes, and unstructured terrains further verified that the MORPH wheel passively adjusts its radius to provide optimal transmission ratio. The MORPH wheel exemplifies a mechanically programmed structure, embedding intelligent, context-dependent behavior directly into its physical design. This approach offers a new paradigm for passive variable transmission and mechanical intelligence in robotic mobility systems operating in unpredictable or control-limited environments.

Numerous studies have attempted to develop medical devices using vine robots due to their potential for frictionless locomotion and adaptability in confined environments. However, for applications in colonoscopy, challenges such as high stiffness, limited steering capabilities, difficulties in integrating tethered sensors, and issues related to safe retraction have hindered their practical application. This article addresses these challenges and presents a comprehensive solution that simultaneously resolves these issues while preserving the intrinsic features of vine robots. We propose a novel soft robotic endoscope that leverages an optimized eversion mechanism to maintain low stiffness and ensure compliance with the natural curvature of the colon, minimizing bowel distension. To enable real-time imaging, we introduce a passive tethered camera stabilization system that secures the camera at the distal tip with minimal internal tension. Additionally, the device integrates active steering capabilities using fabric pneumatic artificial muscles, allowing for precise two-degree-of-freedom steering to navigate through complex pathways. A non-sealed, self-retractable mechanism ensures safe and reliable retraction by preventing buckling while maintaining the robot's compliance, even with an embedded tethered sensor inside the inner channel. Comprehensive characterization of key parameters, such as vine diameter and retraction channel geometry, further enhances the system's performance in endoscopic applications. The effectiveness of the proposed endoscope was validated through extensive testing in endoscopic phantom models and in vivo trials, demonstrating significant reductions in insertion forces and colon deformation compared with conventional endoscopes. In phantom studies, the device demonstrated an 80% reduction in mesentery extension compared with a conventional flexible endoscope. In vivo, the soft growing endoscope (SGE) reached the ileocecal valve within 2 min while maintaining real-time imaging, internal channel integrity, and buckling-free retraction. By overcoming key challenges in adapting vine robots for endoscopy, this SGE offers a minimally invasive, safer, and more effective solution for colonoscopy, enhancing patient comfort and procedural efficiency while reducing physical strain on physicians.

Conventional soft robot actuators excel in compliance, but their uncontrolled deformations compromise accuracy and hinder scaling to multi-degree-of-freedom (DoF) systems. We introduce a MONOlithic ORIGAMI-inspired soft folding actuator design (MONORIGAMI) that establishes a design strategy based on spatially programmed stiffness anisotropy to preserve material compliance along desired folding directions while selectively restricting deformation in unwanted directions. The actuator leverages stiffness tiers based on material thickness, patterned in an origami-inspired geometry with facets and creases, converting unconstrained soft deformation into accurate, repeatable, and composable folding motions without additional reinforcements. The design is fully 3D-printable through a single-material, single-print process that requires no assembly. Each actuator serves as a scalable motion primitive, and linking and orienting multiple actuators mechanically programs multi-DoF trajectories. Using the same fundamental module, we demonstrate three 3D-printed soft multi-DoF robotic systems spanning distinct application domains: (1) a compact 4-DoF wearable haptic device for high-fidelity cutaneous feedback in virtual reality (VR), (2) a 3-DoF joystick for kinesthetic feedback in teleoperation, and (3) a modular robotic gripper capable of underwater operation with geometry-encoded grasp trajectories. These systems demonstrate the module's capabilities for compact multi-axis integration, controlled physical interaction, and geometry-programmed operation across different environments. Together, these results show that MONORIGAMI provides a general, composable, accessible, reliable, and scalable platform for high-precision soft multi-DoF robotics, addressing long-standing limitations in both soft actuator design and fabrication.

Soft growing robots possess unique advantages, such as the ability to navigate confined and complex environments. Although securing a stable inner channel is essential for enabling a wide range of applications, such as sensor integration, tool passage, and material transfer, the pressure required for eversion based growth imposes compressive loading on the channel, causing it to collapse by constriction. Existing approaches for channel stabilization rely on auxiliary actuation or complex control, which introduce leakage, deformation, or limited scalability. To over come these limitations, this paper presents an origami-inspired mechanism embedded into the robot membrane that inherently forms a structurally rigid inner channel without additional actuation, while preserving the intrinsic benefits of soft growing robots. The design further enables user-defined customization of channel geometry to meet application-specific requirements. A comprehensive modeling framework is developed to characterize the geometric and mechanical behavior of the mechanism, and its validity is confirmed through simulations and comparative experiments against conventional soft growing robots. Demonstrations, including inner channel visualization, steering, growth through confined paths, and parameter-dependent scalability, validate the practicality and versatility of the proposed approach.

Soft growing robots are being used in various fields owing to their distinct advantages. However, their ability to manipulate tools in different applications is still challenging. In this letter, we propose an inflatable-structure-based working-channel securing mechanism for soft growing robots. The proposed mechanism provides a solution for securing a stable and accessible working channel with pressure equal to the atmospheric pressure, while maintaining the unique advantages of soft growing robots. The proposed soft growing robot can freely transfer materials and tools through its interior channel; therefore, it can adapt and replace equipment based on specific work requirements. This capability enhances the versatility and efficiency of the robot in various applications. Prototyping and experimental validation were conducted to show the performance and capabilities of the robot. The results of the experiments demonstrated that the soft growing robot effectively secured the working channel, enabling the transfer of materials and tools without interference from the inflation pressure. The accessibility of the secured channel was validated through slide-plate and pipe-pulling experiments. The demonstration of the growing mechanism confirmed the ability of the robot to secure a working channel during its growth, whereas the steering demonstration showcased its inherent steering function.