Shape-Aware Whole-Body Control for Continuum Robots with Application in Endoluminal Surgical Robotics

This paper presents a shape-aware whole-body control framework for tendon-driven continuum robots with direct application to endoluminal surgical navigation. Endoluminal procedures, such as bronchoscopy, demand precise and safe navigation through tortuous, patient-specific anatomy where conventional tip-only control often leads to wall contact, tissue trauma, or failure to reach distal targets. To address these challenges, our approach combines a physics-informed backbone model with residual learning through an Augmented Neural ODE, enabling accurate shape estimation and efficient Jacobian computation. A sampling-based Model Predictive Path Integral (MPPI) controller leverages this representation to jointly optimize tip tracking, backbone conformance, and obstacle avoidance under actuation constraints. A task manager further enhances adaptability by allowing real-time adjustment of objectives, such as wall clearance or direct advancement, during tele-operation. Extensive simulation studies demonstrate millimeter-level accuracy across diverse scenarios, including trajectory tracking, dynamic obstacle avoidance, and shape-constrained reaching. Real-robot experiments on a bronchoscopy phantom validate the framework, showing improved lumen-following accuracy, reduced wall contacts, and enhanced adaptability compared to joystick-only navigation and existing baselines. These results highlight the potential of the proposed framework to increase safety, reliability, and operator efficiency in minimally invasive endoluminal surgery, with broader applicability to other confined and safety-critical environments.

ROOM: A Physics-Based Continuum Robot Simulator for Photorealistic Medical Datasets Generation

Continuum robots are advancing bronchoscopy procedures by accessing complex lung airways and enabling targeted interventions. However, their development is limited by the lack of realistic training and test environments: Real data is difficult to collect due to ethical constraints and patient safety concerns, and developing autonomy algorithms requires realistic imaging and physical feedback. We present ROOM (Realistic Optical Observation in Medicine), a comprehensive simulation framework designed for generating photorealistic bronchoscopy training data. By leveraging patient CT scans, our pipeline renders multi-modal sensor data including RGB images with realistic noise and light specularities, metric depth maps, surface normals, optical flow and point clouds at medically relevant scales. We validate the data generated by ROOM in two canonical tasks for medical robotics -- multi-view pose estimation and monocular depth estimation, demonstrating diverse challenges that state-of-the-art methods must overcome to transfer to these medical settings. Furthermore, we show that the data produced by ROOM can be used to fine-tune existing depth estimation models to overcome these challenges, also enabling other downstream applications such as navigation. We expect that ROOM will enable large-scale data generation across diverse patient anatomies and procedural scenarios that are challenging to capture in clinical settings. Code and data: https://github.com/iamsalvatore/room.

Augmented Bridge Spinal Fixation: A New Concept for Addressing Pedicle Screw Pullout via a Steerable Drilling Robot and Flexible Pedicle Screws

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.

S3D: A Spatial Steerable Surgical Drilling Framework for Robotic Spinal Fixation Procedures

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.

Towards Design and Development of a Concentric Tube Steerable Drilling Robot for Creating S-shape Tunnels for Pelvic Fixation Procedures

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.

A Synergistic Framework for Learning Shape Estimation and Shape-Aware Whole-Body Control Policy for Continuum Robots

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.

Towards an Autonomous Minimally Invasive Spinal Fixation Surgery Using a Concentric Tube Steerable Drilling Robot

Towards performing a realistic autonomous minimally invasive spinal fixation procedure, in this paper, we introduce a unique robotic drilling system utilizing a concentric tube steerable drilling robot (CT-SDR) integrated with a seven degree-of-freedom robotic manipulator. The CT-SDR in integration with the robotic arm enables creating precise J-shape trajectories enabling access to the areas within the vertebral body that currently are not accessible utilizing existing rigid instruments. To ensure safety and accuracy of the autonomous drilling procedure, we also performed required calibration procedures. The performance of the proposed robotic system and the calibration steps were thoroughly evaluated by performing various drilling experiments on simulated Sawbone samples.

A Patient-Specific Framework for Autonomous Spinal Fixation via a Steerable Drilling Robot

In this paper, with the goal of enhancing the minimally invasive spinal fixation procedure in osteoporotic patients, we propose a first-of-its-kind image-guided robotic framework for performing an autonomous and patient-specific procedure using a unique concentric tube steerable drilling robot (CT-SDR). Particularly, leveraging a CT-SDR, we introduce the concept of J-shape drilling based on a pre-operative trajectory planned in CT scan of a patient followed by appropriate calibration, registration, and navigation steps to safely execute this trajectory in real-time using our unique robotic setup. To thoroughly evaluate the performance of our framework, we performed several experiments on two different vertebral phantoms designed based on CT scan of real patients.

Spatial Spinal Fixation: A Transformative Approach Using a Unique Robot-Assisted Steerable Drilling System and Flexible Pedicle Screw

Spinal fixation procedures are currently limited by the rigidity of the existing instruments and pedicle screws leading to fixation failures and rigid pedicle screw pull out. Leveraging our recently developed Concentric Tube Steerable Drilling Robot (CT-SDR) in integration with a robotic manipulator, to address the aforementioned issue, here we introduce the transformative concept of Spatial Spinal Fixation (SSF) using a unique Flexible Pedicle Screw (FPS). The proposed SSF procedure enables planar and out-of-plane placement of the FPS throughout the full volume of the vertebral body. In other words, not only does our fixation system provide the option of drilling in-plane and out-of-plane trajectories, it also enables implanting the FPS inside linear (represented by an I-shape) and/or non-linear (represented by J-shape) trajectories. To thoroughly evaluate the functionality of our proposed robotic system and the SSF procedure, we have performed various experiments by drilling different I-J and J-J drilling trajectory pairs into our custom-designed L3 vertebral phantoms and analyzed the accuracy of the procedure using various metrics.

Towards Biomechanics-Aware Design of a Steerable Drilling Robot for Spinal Fixation Procedures with Flexible Pedicle Screws

Towards reducing the failure rate of spinal fixation surgical procedures in osteoporotic patients, we propose a unique biomechanically-aware framework for the design of a novel concentric tube steerable drilling robot (CT-SDR). The proposed framework leverages a patient-specific finite element (FE) biomechanics model developed based on Quantitative Computed Tomography (QCT) scans of the patient's vertebra to calculate a biomechanically-optimal and feasible drilling and implantation trajectory. The FE output is then used as a design requirement for the design and evaluation of the CT-SDR. Providing a balance between the necessary flexibility to create curved optimal trajectories obtained by the FE module with the required strength to not buckle during drilling through a hard simulated bone material, we showed that the CT-SDR can reliably recreate this drilling trajectory with errors between 1.7-2.2%