Farshid Alambeigi

Autonomous robotic-assisted surgery (RAS) has emerged as a promising objective in biomedical technology, further enhanced by miniaturization toward microrobotic-assisted surgery (μ-RAS). This reduction in scale promises minimally invasive, partially or fully automated surgical procedures, with the potential to reduce patient recovery times, lower medical costs, and enable previously unavailable procedural options. This perspective highlights the specific advances in RAS that potentially map to the microscale (μ-RAS), organized across five surgical domains: endovascular, endoluminal, laparoscopic, ophthalmic, and orthopedic. We examine both clinical demands and technological advances in surgical robotics and identify the key innovations required for progress across these surgical fields. Our contribution is distinct in combining the perspectives of both surgical experts and bioengineering innovators, outlining a roadmap for the advancement and eventual integration of autonomous RAS and μ-RAS into mainstream surgical practice.

The Python robotics ecosystem faces a challenge: while many libraries exist for rigid body transformations, few are both lightweight and mathematically strict. This paper introduces SE3Kit, a lightweight Python library efficient operations on the Special Euclidean Group SE(3) and the Special Orthogonal Group SO(3). Unlike established frameworks that require heavy dependencies (e.g., SpatialMath, PyPose) or general tools that lack robotics-specific features (e.g., SciPy), SE3Kit targets the gap between these extremes. It is designed for embedded deployment, rapid prototyping, and education while providing rigorous mathematical implementation. It provides a pure-Python, NumPy-only implementation of Lie Group operations, without the overhead of deep learning or other visualization software.

The Python robotics ecosystem faces a challenge: while many libraries exist for rigid body transformations, few are both lightweight and mathematically strict. This paper introduces SE3Kit, a lightweight Python library efficient operations on the Special Euclidean Group SE(3) and the Special Orthogonal Group SO(3). Unlike established frameworks that require heavy dependencies (e.g., SpatialMath, PyPose) or general tools that lack robotics-specific features (e.g., SciPy), SE3Kit targets the gap between these extremes. It is designed for embedded deployment, rapid prototyping, and education while providing rigorous mathematical implementation. It provides a pure-Python, NumPy-only implementation of Lie Group operations, without the overhead of deep learning or other visualization software.

Autonomous medical robots hold promise to improve patient outcomes, reduce provider workload, democratize access to care, and enable superhuman precision. However, autonomous medical robotics has been limited by a fundamental data problem: existing medical robotic datasets are small, single-embodiment, and rarely shared openly, restricting the development of foundation models that the field needs to advance. We introduce Open-H-Embodiment, the largest open dataset of medical robotic video with synchronized kinematics to date, spanning more than 50 institutions and multiple robotic platforms including the CMR Versius, Intuitive Surgical's da Vinci, da Vinci Research Kit (dVRK), Rob Surgical BiTrack, Virtual Incision's MIRA, Moon Surgical Maestro, and a variety of custom systems, spanning surgical manipulation, robotic ultrasound, and endoscopy procedures. We demonstrate the research enabled by this dataset through two foundation models. GR00T-H is the first open foundation vision-language-action model for medical robotics, which is the only evaluated model to achieve full end-to-end task completion on a structured suturing benchmark (25% of trials vs. 0% for all others) and achieves 64% average success across a 29-step ex vivo suturing sequence. We also train Cosmos-H-Surgical-Simulator, the first action-conditioned world model to enable multi-embodiment surgical simulation from a single checkpoint, spanning nine robotic platforms and supporting in silico policy evaluation and synthetic data generation for the medical domain. These results suggest that open, large-scale medical robot data collection can serve as critical infrastructure for the research community, enabling advances in robot learning, world modeling, and beyond.

Autonomous medical robots hold promise to improve patient outcomes, reduce provider workload, democratize access to care, and enable superhuman precision. However, autonomous medical robotics has been limited by a fundamental data problem: existing medical robotic datasets are small, single-embodiment, and rarely shared openly, restricting the development of foundation models that the field needs to advance. We introduce Open-H-Embodiment, the largest open dataset of medical robotic video with synchronized kinematics to date, spanning more than 50 institutions and multiple robotic platforms including the CMR Versius, Intuitive Surgical's da Vinci, da Vinci Research Kit (dVRK), Rob Surgical BiTrack, Virtual Incision's MIRA, Moon Surgical Maestro, and a variety of custom systems, spanning surgical manipulation, robotic ultrasound, and endoscopy procedures. We demonstrate the research enabled by this dataset through two foundation models. GR00T-H is the first open foundation vision-language-action model for medical robotics, which is the only evaluated model to achieve full end-to-end task completion on a structured suturing benchmark (25% of trials vs. 0% for all others) and achieves 64% average success across a 29-step ex vivo suturing sequence. We also train Cosmos-H-Surgical-Simulator, the first action-conditioned world model to enable multi-embodiment surgical simulation from a single checkpoint, spanning nine robotic platforms and supporting in silico policy evaluation and synthetic data generation for the medical domain. These results suggest that open, large-scale medical robot data collection can serve as critical infrastructure for the research community, enabling advances in robot learning, world modeling, and beyond.

In this paper, we introduce an autonomous Ultrasonic Sacral Osteotomy (USO) robotic system that integrates an ultrasonic osteotome with a seven-degree-of-freedom (DoF) robotic manipulator guided by an optical tracking system. To assess multi-directional control along both the surface trajectory and cutting depth of this system, we conducted quantitative comparisons between manual USO (MUSO) and robotic USO (RUSO) in Sawbones phantoms under identical osteotomy conditions. The RUSO system achieved sub-millimeter trajectory accuracy (0.11 mm RMSE), an order of magnitude improvement over MUSO (1.10 mm RMSE). Moreover, MUSO trials showed substantial over-penetration (16.0 mm achieved vs. 8.0 mm target), whereas the RUSO system maintained precise depth control (8.1 mm). These results demonstrate that robotic procedures can effectively overcome the critical limitations of manual osteotomy, establishing a foundation for safer and more precise sacral resections.

This study presents the analytical modeling, design, fabrication, and experimental evaluation of a pufferfishinspired soft robot for pipe inspection applications. This inflatable soft robot employs vibration as its sole locomotion mechanism, with internal pressure serving as a control variable to (i) tune the stiffness and natural frequency of the inflatable structure, (ii) optimize vibration transmission and locomotion performance, and (iii) enable locomotion in varying-size pipes. To investigate the influence of vibration on locomotion, we develop a vibration model and analyze the effects of internal pressure, material properties, excitation frequency, and geometric design. Locomotion performance of the proposed robot is validated experimentally using four different prototypes with varying material stiffness and geometry. Results show that the optimized design can achieve locomotion speeds exceeding $100 \mathrm{~mm} / \mathrm{s}$ in both horizontal and vertical tubes with varying diameters. This work paves the way for optimizing vibrationbased soft robot morphology to achieve adaptive locomotion in confined and dynamic environments.

Colorectal cancer screening critically depends on colonoscopy, yet existing platforms offer limited support for systematically studying the coupled dynamics of operator control, instrument motion, and visual feedback. This gap restricts reproducible closed-loop research in robotic colonoscopy, medical imaging, and emerging vision-language-action (VLA) learning paradigms. To address this challenge, we present OpenRC, an open-source modular robotic colonoscopy framework that retrofits conventional scopes while preserving clinical workflow. The framework supports simultaneous recording of video, operator commands, actuation state, and distal tip pose. We experimentally validated motion consistency and quantified cross-modal latency across sensing streams. Using this platform, we collected a multimodal dataset comprising 1,894 teleoperated episodes ~19 hours across 10 structured task variations of routine navigation, failure events, and recovery behaviors. By unifying open hardware and an aligned multimodal dataset, OpenRC provides a reproducible foundation for research in multimodal robotic colonoscopy and surgical autonomy.

Colorectal cancer screening critically depends on colonoscopy, yet existing platforms offer limited support for systematically studying the coupled dynamics of operator control, instrument motion, and visual feedback. This gap restricts reproducible closed-loop research in robotic colonoscopy, medical imaging, and emerging vision-language-action (VLA) learning paradigms. To address this challenge, we present OpenRC, an open-source modular robotic colonoscopy framework that retrofits conventional scopes while preserving clinical workflow. The framework supports simultaneous recording of video, operator commands, actuation state, and distal tip pose. We experimentally validated motion consistency and quantified cross-modal latency across sensing streams. Using this platform, we collected a multimodal dataset comprising 1,894 teleoperated episodes ~19 hours across 10 structured task variations of routine navigation, failure events, and recovery behaviors. By unifying open hardware and an aligned multimodal dataset, OpenRC provides a reproducible foundation for research in multimodal robotic colonoscopy and surgical autonomy.

Sacral tumors often necessitate surgical resection via En Bloc Sacrectomy, a procedure requiring sub-millimeter precision to ensure complete tumor removal while preserving critical nerve roots. To address the inherent risks and limitations of conventional freehand techniques, this paper introduces a novel semi-autonomous robotic system for high-precision partial sacrectomy. The platform integrates a seven degree-of-freedom robotic manipulator with a commercial ultrasonic osteotome, an instrument specifically designed to cut hard bone while minimizing soft tissue damage. We conducted a comprehensive quantitative characterization of the system, demonstrating that sub-millimeter trajectory accuracy (Root Mean Squared Error ≤ 0.13 mm) is consistently maintained across a wide range of cutting speeds, from 0.5 mm/s to 3 mm/s. This robust performance enabled a significant reduction in total osteotomy procedure time (down to 28 s) with maximum interaction forces remaining low (1 N). Furthermore, the framework’s capability was validated in a clinically relevant case study on a custom CNC-machined human sacrum phantom, where the system executed complex, multi-pass cuts with an accuracy of 0.12 mm. These results confirm the system’s stability, speed, and precision, representing a significant technological step toward enabling safer and more efficient robotic assistance for complex orthopedic oncology procedures.

Current approaches for diagnosing cancers of the gastrointestinal (GI) tract using artificial intelligence are limited by the scarcity and inherent biases of medical data, which lead to missed diagnoses. While conventional data augmentation can address class imbalance, it often fails to improve underlying data quality or diversity. To address these limitations, this work introduces a data-valuation framework to understand the intrinsic value of each data point. Leveraging a novel vision-based tactile sensor (VTS) capable of capturing high-resolution textural images, we employ data valuation to quantify the contribution of each sample to the downstream GI tumor classification task. We benchmark two complementary methods, Data Valuation via Gradient Similarity (DVGS) and TracIn, to assign a “worth” to each textural image and validate these scores through targeted data removal experiments. Our results demonstrate that data valuation effectively stratifies our dataset. The methods consistently assign high value to rare and morphologically distinct samples while identifying redundancy in more common classes. We demonstrate that removing these high-value samples leads to a significantly sharper degradation in both predictive accuracy and inter-class fairness compared to random removal. Using the same valuation approach, we examine the inductive biases of diverse architectures and uncover how convolutional and transformer-based networks prioritize different features within the same dataset. Our results show that agreements between valuation methods and models vary substantially, revealing architecture-dependent sensitivities to texture and surface contact. This study establishes a principled methodology for dataset curation based on quantified sample values. By identifying the characteristics of the most valuable data, we also provide a clear pathway towards guiding generative models in synthesizing data that is demonstrably effective, representing a crucial step toward engineering more robust, data-efficient, and equitable AI systems for critical medical applications.

Conventional sacroiliac (SI) screw fixation relies on rigid drills and linear implant trajectories, which limits access to regions of high bone mineral density and contributes to screw loosening, misplacement, and neurovascular injury - particularly in osteoporotic patients. To address this issue, we present a Flexible Sacroiliac Screw (FSIS) and a complementary steerable drilling robot that enable SI joint fixation along curvilinear trajectories. We evaluate both systems by drilling J-shape trajectories and fixating our flexible implant into Sawbone phantoms with different densities simulating cortical and cancellous regions inside the bone. Moreover, we perform a case study on a CNC-machined phantom further demonstrating the system’s ability to accurately traverse the ilium, follow a curvilinear sacral corridor, and maintain safe boundaries under fluoroscopic visualization.

This report summarizes the outcomes and impact of the IC² program, which has enabled a comprehensive research effort at the intersection of AI-driven medical imaging, generative modeling, and robotic sensing for gastrointestinal cancer applications. The support from this award has directly facilitated the development of new algorithms, datasets, and hardware platforms aimed at improving early detection and diagnosis in minimally invasive procedures.

This paper introduces a novel shape-sensing approach for Concentric Tube Steerable Drilling Robots (CT-SDRs) based on Optical Frequency Domain Reflectometry (OFDR). Unlike traditional FBG-based methods, OFDR enables continuous strain measurement along the entire fiber length with enhanced spatial resolution. In the proposed method, a Shape Sensing Assembly (SSA) is first fabricated by integrating a single OFDR fiber with a flat NiTi wire. The calibrated SSA is then routed through and housed within the internal channel of a flexible drilling instrument, which is guided by the pre-shaped NiTi tube of the CT-SDR. In this configuration, the drilling instrument serves as a protective sheath for the SSA during drilling, eliminating the need for integration or adhesion to the instrument surface that is typical of conventional optical sensor approaches. The performance of the proposed SSA, integrated within the cannulated CT-SDR, was thoroughly evaluated under free-bending conditions and during drilling along multiple J-shaped trajectories in synthetic Sawbones phantoms. Results demonstrate accurate and reliable shape-sensing capability, confirming the feasibility and robustness of this integration strategy.

This paper introduces a novel shape-sensing approach for Concentric Tube Steerable Drilling Robots (CT-SDRs) based on Optical Frequency Domain Reflectometry (OFDR). Unlike traditional FBG-based methods, OFDR enables continuous strain measurement along the entire fiber length with enhanced spatial resolution. In the proposed method, a Shape Sensing Assembly (SSA) is first fabricated by integrating a single OFDR fiber with a flat NiTi wire. The calibrated SSA is then routed through and housed within the internal channel of a flexible drilling instrument, which is guided by the pre-shaped NiTi tube of the CT-SDR. In this configuration, the drilling instrument serves as a protective sheath for the SSA during drilling, eliminating the need for integration or adhesion to the instrument surface that is typical of conventional optical sensor approaches. The performance of the proposed SSA, integrated within the cannulated CT-SDR, was thoroughly evaluated under free-bending conditions and during drilling along multiple J-shaped trajectories in synthetic Sawbones phantoms. Results demonstrate accurate and reliable shape-sensing capability, confirming the feasibility and robustness of this integration strategy.

Continuum manipulators (CMs) are widely used in minimally invasive procedures due to their compliant structure and ability to navigate deep and confined anatomical environments. However, their distributed deformation makes force sensing, contact detection, localization, and force estimation challenging, particularly when interactions occur at unknown arc-length locations along the robot. To address this problem, we propose a cascade learning-based framework (CLF) for CMs instrumented with a single distributed Optical Frequency Domain Reflectometry (OFDR) fiber embedded along one side of the robot. The OFDR sensor provides dense strain measurements along the manipulator backbone, capturing strain perturbations caused by external interactions. The proposed CLF first detects contact using a Gradient Boosting classifier and then estimates contact location and interaction force magnitude using a CNN--FiLM model that predicts a spatial force distribution along the manipulator. Experimental validation on a sensorized tendon-driven CM in an obstructed environment demonstrates that a single distributed OFDR fiber provides sufficient information to jointly infer contact occurrence, location, and force in continuum manipulators.

Continuum manipulators (CMs) are widely used in minimally invasive procedures due to their compliant structure and ability to navigate deep and confined anatomical environments. However, their distributed deformation makes force sensing, contact detection, localization, and force estimation challenging, particularly when interactions occur at unknown arc-length locations along the robot. To address this problem, we propose a cascade learning-based framework (CLF) for CMs instrumented with a single distributed Optical Frequency Domain Reflectometry (OFDR) fiber embedded along one side of the robot. The OFDR sensor provides dense strain measurements along the manipulator backbone, capturing strain perturbations caused by external interactions. The proposed CLF first detects contact using a Gradient Boosting classifier and then estimates contact location and interaction force magnitude using a CNN--FiLM model that predicts a spatial force distribution along the manipulator. Experimental validation on a sensorized tendon-driven CM in an obstructed environment demonstrates that a single distributed OFDR fiber provides sufficient information to jointly infer contact occurrence, location, and force in continuum manipulators.

Gastric cancer diagnosis faces challenges due to inter-class variability and limited data availability. Vision-based Tactile Sensors (VTS) provide high-resolution textural imaging and tactile information important for tumor classification, but data collection is time-consuming and degrades sensor quality. To overcome this, we explore a hybrid approach that combines physics-based VTS simulation with a training-free diffusion-based style transfer technique for fast, inexpensive and controllable generation of high-fidelity synthetic VTS images of gastric cancer tumors. We systematically evaluate the impact of style injection parameters on realism using dataset- and image-level metrics. Our findings reveal a trade-off between realism and clinical utility, offering an important step towards optimizing simulator-based synthetic data generation to bridge the domain gap for the classification of advanced gastric cancer tumors.

Vision-based Tactile Sensors (VTS) are resilient to external lighting conditions and visual occlusions. However, internal factors such as the sensor’s geometry and fabrication procedure, the intensity, power, manufacturer, and placement of used LEDs, as well as camera specifications can directly affect and introduce significant variations in the illumination of the resulting textural images. In this study, we investigate the impact of the illumination variations of a VTS on the performance of AI models classifying Advanced Gastric Cancer (AGC) tumors. In particular, we demonstrate the importance of color-centric data augmentation techniques on the classification accuracy of an AI model by (1) comparing classifier performance trained on colored and grayscale VTS textural images, and (2) evaluating the generalization ability of our classifiers on images synthetically modified by adjusting hue, brightness, contrast and saturation. Further, we conduct a sensitivity analysis to determine which of these color-centric augmentations most effectively enhance AI model performance in AGC diagnosis.

In this paper, we introduce an autonomous Ultrasonic Sacral Osteotomy (USO) robotic system that integrates an ultrasonic osteotome with a seven-degree-of-freedom (DoF) robotic manipulator guided by an optical tracking system. To assess multi-directional control along both the surface trajectory and cutting depth of this system, we conducted quantitative comparisons between manual USO (MUSO) and robotic USO (RUSO) in Sawbones phantoms under identical osteotomy conditions. The RUSO system achieved sub-millimeter trajectory accuracy (0.11 mm RMSE), an order of magnitude improvement over MUSO (1.10 mm RMSE). Moreover, MUSO trials showed substantial over-penetration (16.0 mm achieved vs. 8.0 mm target), whereas the RUSO system maintained precise depth control (8.1 mm). These results demonstrate that robotic procedures can effectively overcome the critical limitations of manual osteotomy, establishing a foundation for safer and more precise sacral resections.

In this paper, we introduce an autonomous Ultrasonic Sacral Osteotomy (USO) robotic system that integrates an ultrasonic osteotome with a seven-degree-of-freedom (DoF) robotic manipulator guided by an optical tracking system. To assess multi-directional control along both the surface trajectory and cutting depth of this system, we conducted quantitative comparisons between manual USO (MUSO) and robotic USO (RUSO) in Sawbones phantoms under identical osteotomy conditions. The RUSO system achieved sub-millimeter trajectory accuracy (0.11 mm RMSE), an order of magnitude improvement over MUSO (1.10 mm RMSE). Moreover, MUSO trials showed substantial over-penetration (16.0 mm achieved vs. 8.0 mm target), whereas the RUSO system maintained precise depth control (8.1 mm). These results demonstrate that robotic procedures can effectively overcome the critical limitations of manual osteotomy, establishing a foundation for safer and more precise sacral resections.

Robotic platforms are increasingly being integrated into medical imaging applications [1]. We propose a gantry-less SPECT imaging platform consisting of a collaborative high-articulated robotic manipulator (KUKA LBR Med 14 R820), a room-temperature semiconductor detector panel (<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$3 \times 35-\text{mm}$</tex> thick CZT detectors), and a custom multi-pinhole tungsten collimator. The system first acquires a preliminary low-resolution image to determine an optimized trajectory around the object under examination. Then, the robotic manipulator dynamically places the detector panel to acquire projections and reconstruct a high-resolution tomographic image, according to the principle of adaptive imaging [2].

Current pelvic fixation techniques rely on rigid drilling tools, which inherently constrain the placement of rigid medical screws in the complex anatomy of pelvis. These constraints prevent medical screws from following anatomically optimal pathways and force clinicians to fixate screws in linear trajectories. This suboptimal approach, combined with the unnatural placement of the excessively long screws, lead to complications such as screw misplacement, extended surgery times, and increased radiation exposure due to repeated X-ray images taken ensure to safety of procedure. To address these challenges, in this paper, we present the design and development of a unique 4-degree-of-freedom (DoF) pelvic concentric tube steerable drilling robot (pelvic CT-SDR). The pelvic CT-SDR is capable of creating long S-shaped drilling trajectories that follow the natural curvatures of the pelvic anatomy. The performance of the pelvic CT-SDR was thoroughly evaluated through several S-shape drilling experiments in simulated bone phantoms.

In this paper, we introduce S<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup>D: A Spatial Steerable Surgical Drilling Framework for Robotic Spinal Fixation Procedures. S<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup>D is designed to enable realistic steerable drilling while accounting for the anatomical constraints associated with vertebral access in spinal fixation (SF) procedures. To achieve this, we first enhanced our previously designed concentric tube Steerable Drilling Robot (CT-SDR) to facilitate steerable drilling across all vertebral levels of the spinal column. Additionally, we propose a four-Phase calibration, registration, and navigation procedure to perform realistic SF procedures on a spine holder phantom by integrating the CT-SDR with a seven-degree-of-freedom robotic manipulator. The functionality of this framework is validated through planar and out-of-plane steerable drilling experiments in vertebral phantoms.

To address the screw loosening and pullout limitations of rigid pedicle screws in spinal fixation procedures, and to leverage our recently developed Concentric Tube Steerable Drilling Robot (CT-SDR) and Flexible Pedicle Screw (FPS), in this paper, we introduce the concept of Augmented Bridge Spinal Fixation (AB-SF). In this concept, two connecting J-shape tunnels are first drilled through pedicles of vertebra using the CT-SDR. Next, two FPSs are passed through this tunnel and bone cement is then injected through the cannulated region of the FPS to form an augmented bridge between two pedicles and reinforce strength of the fixated spine. To experimentally analyze and study the feasibility of AB-SF technique, we first used our robotic system (i.e., a CT-SDR integrated with a robotic arm) to create two different fixation scenarios in which two J-shape tunnels, forming a bridge, were drilled at different depth of a vertebral phantom. Next, we implanted two FPSs within the drilled tunnels and then successfully simulated the bone cement augmentation process.

Spinal fixation procedures rely on pedicle screws to stabilize the vertebral column, but conventional rigid pedicle screws (RPS) face challenges such as misplacement, pullout, and loosening, particularly in patients with low bone mineral density (BMD). To overcome these limitations, we recently proposed a flexible pedicle screw (FPS) inserted inside a J-shape trajectory drilled by a steerable drilling robot. Towards biomechanical evaluation of our proposed FPS for spinal fixation procedures, in this paper, we introduce the design, integration, calibration, and evaluation of an optical frequency domain reflectometry (OFDR) strain sensor into an FPS. This sensor-integrated FPS (Si-FPS) provides real-time strain and shape-sensing information, facilitating improved implant functionality assessment and optimization. To thoroughly evaluate the Si-FPS, we first additively manufacture a special FPS and integrate a OFDR shape sensing assembly within its structure. We then assess shape sensing performance of this sensorized FPS using static and dynamic FPS insertion experiments.

In this study, we introduce a new concept for reconstruction of Volumetric Muscle Loss (VML) injuries and propose the spatial robotic embedded bioprinting technique. As opposed to the traditional layer-by-layer printing, we leverage the support-free nature of embedded bioprinting to print spatial and complex structures of fascicles in a fusiform muscle. To demonstrate feasibility of this concept, we first propose our robotic bioprinting framework including a robotic arm integrated with a custom-designed bioprinting injector. Complementary motion planning algorithms uniquely designed for this printing task are further proposed. Moreover, the effect of embedded bioprinting parameters, as well as the supporting bath and injecting materials compatibility on the uniformity and quality of the printed constructs has been analyzed. Finally, we perform a case study by printing a fusiform muscle-shape construct using the proposed concept and algorithms, and evaluate the quality of the printed structure.

In this study, we introduce a simulation-based modeling framework for the optimal design of our recently developed inflatable endoscopic vision-based tactile sensing balloon (E-VTSB). Of note, E-VTSB is designed for providing a safe and high-resolution textural mapping and morphology characterization of colorectal cancer (CRC) polyps to enhance the early diagnosis of cancerous polyps. Leveraging the Simulation Open Framework Architecture (SOFA) software and by performing complementary experimental validation, we thoroughly analyzed and investigated the impact of the elastic modulus of the material constitution of E-VTSB on its deformation behavior under different applied pressures. Our findings revealed a close correlation between the simulated outcomes and experimental data performed on two different E-VTSBs. In particular, with the maximum absolute deformation error of <12%, our results clearly validated the proposed framework’s accuracy in predicting the E-VTSB’s deformation trend and its potential use for optimizing the design parameters.

Despite the widespread adoption of endoscopic devices for several cancer screening procedures, manual control of these devices still remains challenging for clinicians, leading to several critical issues such as increased workload, fatigue, and distractions. To address these issues, in this paper, we introduce the design and development of an intuitive, modular, and easily installable mechatronic framework. This framework includes (i) a novel nested collet-chuck gripping mechanism that can readily be integrated and assembled with the existing endoscopic devices and control their bending degrees-of-freedom (DoFs); (ii) a feeder mechanism that can control the insertion/retraction DoF of a colonoscope, and (iii) a complementary and intuitive user interface that enables simultaneous control of all DoFs during the procedure. To analyze the design of the proposed mechanisms, we also introduce a mathematical modeling approach and a design space for optimal selection of the parameters involved in the design of gripping and feeder mechanisms. Our simulation and experimental studies thoroughly demonstrate the performance of the proposed mathematical modeling and robotic framework.

To address the screw loosening and pullout limitations of rigid pedicle screws in spinal fixation procedures, and to leverage our recently developed Concentric Tube Steerable Drilling Robot (CT-SDR) and Flexible Pedicle Screw (FPS), in this paper, we introduce the concept of Augmented Bridge Spinal Fixation (AB-SF). In this concept, two connecting J-shape tunnels are first drilled through pedicles of vertebra using the CT-SDR. Next, two FPSs are passed through this tunnel and bone cement is then injected through the cannulated region of the FPS to form an augmented bridge between two pedicles and reinforce strength of the fixated spine. To experimentally analyze and study the feasibility of AB-SF technique, we first used our robotic system (i.e., a CT-SDR integrated with a robotic arm) to create two different fixation scenarios in which two J-shape tunnels, forming a bridge, were drilled at different depth of a vertebral phantom. Next, we implanted two FPSs within the drilled tunnels and then successfully simulated the bone cement augmentation process.

Current pelvic fixation techniques rely on rigid drilling tools, which inherently constrain the placement of rigid medical screws in the complex anatomy of pelvis. These constraints prevent medical screws from following anatomically optimal pathways and force clinicians to fixate screws in linear trajectories. This suboptimal approach, combined with the unnatural placement of the excessively long screws, lead to complications such as screw misplacement, extended surgery times, and increased radiation exposure due to repeated X-ray images taken ensure to safety of procedure. To address these challenges, in this paper, we present the design and development of a unique 4 degree-of-freedom (DoF) pelvic concentric tube steerable drilling robot (pelvic CT-SDR). The pelvic CT-SDR is capable of creating long S-shaped drilling trajectories that follow the natural curvatures of the pelvic anatomy. The performance of the pelvic CT-SDR was thoroughly evaluated through several S-shape drilling experiments in simulated bone phantoms.

In this paper, we introduce S3D: A Spatial Steerable Surgical Drilling Framework for Robotic Spinal Fixation Procedures. S3D is designed to enable realistic steerable drilling while accounting for the anatomical constraints associated with vertebral access in spinal fixation (SF) procedures. To achieve this, we first enhanced our previously designed concentric tube Steerable Drilling Robot (CT-SDR) to facilitate steerable drilling across all vertebral levels of the spinal column. Additionally, we propose a four-Phase calibration, registration, and navigation procedure to perform realistic SF procedures on a spine holder phantom by integrating the CT-SDR with a seven-degree-of-freedom robotic manipulator. The functionality of this framework is validated through planar and out-of-plane steerable drilling experiments in vertebral phantoms.

OBJECTIVE: Current spinal fixation (SF) techniques face screw loosening and pullout challenges in osteoporotic patients. This can be attributed to conventional rigid pedicle screws (RPS) being forced to fixate along a constrained linear trajectory into low bone mineral density (BMD) areas of the vertebral body. This study proposes a synergistic patient-specific approach that integrates a steerable drilling robotic system with a novel Flexible Pedicle Screw (FPS) to enhance SF procedures by enabling curved screw fixation. METHODS: A patient-specific framework and synergistic design flowchart were developed to guide the synergistic design of the previously proposed Concentric Tube-Steerable Drilling Robot (CT-SDR) and the FPS. After, the novel FPS is designed based on critical design features and its design is validated using Finite Element Analysis (FEA). The FPS is then fabricated via Direct Metal Laser Sintering (DMLS). The FPS's morphability and self-tapping capability were experimentally assessed in Sawbones phantoms drilled by the CT-SDR system. RESULTS: The FPS successfully morphed to fixate in curvilinear paths, demonstrating effective morphability and self-tapping in simulated bone. CONCLUSION: By enabling a flexible, patient-specific approach to pedicle screw fixation, the FPS and CT-SDR system address key limitations of current SF procedures. This method enhances screw anchorage and fixation strength in osteoporotic vertebrae. SIGNIFICANCE: This work presents a transformative approach to SF, with potential clinical applications in improving surgical outcomes for osteoporotic patients. The integration of robotic-assisted drilling and flexible implants could significantly reduce fixation failure rates, advancing orthopedic and spinal surgical practices.

Human-robot teams (HRTs) in dynamic domains like defense and search & rescue require hierarchical interaction: the ability to consider a human’s role within a hierarchy and environmental context. However, most robot perception systems lack this capability. We present the Command & Control Hierarchical Interface (C<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>HI), a specialized perception framework using Dynamic Bayesian Networks (DBNs) and multiagent assignment to fuse asynchronous, multimodal sensor data. C<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>HI concurrently estimates scene context, human positions, roles, and commands to enable hierarchical awareness, a critical precursor to more advanced teaming behaviors. Using an audiovisual dataset recorded in dynamic social scenes, we demonstrate the framework’s capabilities, notably achieving high visual role recognition accuracy (> 90% after 5 observations using CLIP foundation model) comparable to using a fiducial marker for role recognition. While fiducial commanding was effective (69% accuracy), verbal (31%) and gestural (24%) command recognition proved challenging in these open environments, indicating modality-specific limitations. Our open-source dataset and code encourage further development of hierarchical command and control for HRTs.

To address the challenges associated with shape sensing of continuum manipulators (CMs) using Fiber Bragg Grating (FBG) optical fibers, we present a unique shape sensing assembly utilizing solely a single Optical Frequency Domain Reflectometry (OFDR) fiber attached to a flat nitinol wire (NiTi). Integrating this easy-to-manufacture unique sensor with a long and soft CM with 170 mm length, we performed different experiments to evaluate its C -, J -, and S-shape reconstruction ability. Results demonstrate phenomenal shape reconstruction accuracy for the performed C-shape (<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$&lt;3.14 ~\text{mm}$</tex> tip error, < 2.54 mm shape error), J-shape (<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$&lt;1.91 ~\text{mm}$</tex> tip error, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$&lt;1.11 \mathbf{m m}$</tex> shape error), and S-shape (<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$&lt;\mathbf{1. 7 4 ~ m m}$</tex> tip error, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$&lt;\mathbf{1. 4 0 ~ m m}$</tex> shape error) experiments.

In this paper, we present a novel synergistic framework for learning shape estimation and a shape-aware whole-body control policy for tendon driven continuum robots. Our approach leverages the interaction between two Augmented Neural Ordinary Differential Equations (ANODEs) — the Shape-NODE and Control-NODE — to achieve continuous shape estimation and shape-aware control. The Shape-NODE integrates prior knowledge from Cosserat rod theory, allowing it to adapt and account for model mismatches, while the Control-NODE uses this shape information to optimize a whole-body control policy, trained in a Model Predictive Control (MPC) fashion. This unified framework effectively overcomes limitations of existing data-driven methods, such as poor shape awareness and challenges in capturing complex nonlinear dynamics. Extensive evaluations in both simulation and real-world environments demonstrate the framework's robust performance in shape estimation, trajectory tracking, and obstacle avoidance. The proposed method consistently outperforms state-of-the-art end-to-end, Neural-ODE, and Recurrent Neural Network (RNN) models, particularly in terms of tracking accuracy and generalization capabilities. The code and pretrained models are available at https://github.com/SIRGLab/WholeBodyControl_CTR.

Hysteresis in the tendons driving continuum robots is frequently regarded as a nuisance and a problem that is best avoided. Some prior work seeks to ameliorate the effects of hysteresis through the selection of materials. Others propose models of hysteresis to compensate for their effects. In this work, we present an empirically validated model of hysteresis in tendon-driven continuum robots. We demonstrate that hysteresis contributes to the stability of these robots by mitigating undesirable tensions in robot's backbone. As a result, a model-based approach to hysteresis can be used not just for compensation of a nuisance, but to enhance the utility of continuum robots in safety critical applications such as medical robots.

Aiming at developing a safe, intuitive, and collaborative steerable drilling robotic system for pedicle screw fixation procedures, in this paper, we leverage our recently developed steerable drilling robotic framework, and developed a collaborative drilling mode to control this system. In this control mode, first a user positions a concentric tube steerable drilling robot (CT-SDR) in the workspace and aligns it based on a preplanned trajectory. Next, the CT-SDR is directly controlled by the user through an admittance mode to perform a drilling procedure and creating a J-shape tunnel. To evaluate the user comfort and intuitiveness of the drilling procedure using this system and the proposed control interface, we performed a user study with 11 subjects, who had no prior experience in using this system. The results of this study were analyzed using various qualitative and quantitative metrics.

Robots with adaptive morphology can improve interactions with their environment, allowing adaptive functions without complicated control strategies. Inspired by a pufferfish, this paper introduces PufferFace Robot (PFR), a vibration-propelled soft robot with an adaptive design for exploring cavities using simple locomotion strategies. PFR is particularly useful for inspecting centimeter-scale pipeline systems with varying diameters and shapes, which pose substantial challenges. Although recent soft robots using smart materials offer advantages, difficulties remain in handling different pipe sizes, navigating transitions, and managing fluid flow. PFR's inflatable soft skin is equipped with flexible spikes that create asymmetrical friction under vibrations, propelling the robot forward without feedback control. Its hollow structure allows fluid flow, while a front-mounted camera enhances inspection capabilities. PFR adapts to various pipeline conditions, navigating cavities 1 to 1.5 times its diameter and critical areas such as 90° elbows, T-connectors, and high-curvature sections. In specific scenarios, PFR can generate a propulsive force 20 to 35 times its weight.

3D bioprinting has emerged as a transformative technology in tissue engineering, significantly impacting the creation of patient-specific tissues to enhance clinical outcomes. Despite its rapid advancement, translating this technology from bench to bedside remains a critical clinical need. New bioprinting approaches, such as handheld printers or robotic arm-driven in-situ biofabrication techniques, have emerged as promising alternatives. These advancements enable the reconstruction of damaged tissue directly on living anatomical structures, offering adaptability and precise matching to the affected area. The integration of biomaterials, tissue engineering principles, and digital technologies, particularly robotics, has garnered substantial interest from both academic and industrial sectors, highlighting its potential for clinical applications. However, challenges persist, including refining bioink formulations, adjusting mechanical properties, facilitating in situ crosslinking, and accurately mimicking the extracellular matrix. This review explores the cutting-edge frontier of in situ 3D bioprinting for tissue regeneration, utilizing both handheld and robotic arm-assisted 3D printers. It systematically examines the relative advantages, disadvantages, challenges, and prospects of this technology as it transitions from bench side to bed side.