Dexterous Control of an 11-DOF Redundant Robot for CT-Guided Needle Insertion With Task-Oriented Weighted Policies
Authors: Peihan Zhang, Derek Chen, Ishan Duriseti, Florian Richter, Zoe Chiu, Moira Bohley, Albert Hsiao, Sean Tutton, +2 more
Organizations: Jacobs School of Engineering, University of California San Diego, La Jolla, CA 92093 USA · School of Medicine, University of California San Diego, La Jolla, CA 92093 USA · School of Medicine, University of Missouri-Kansas City, Kansas City, MO 64110 USA
Computed tomography (CT)-guided needle biopsies are critical for diagnosing a range of conditions, including lung cancer, but present challenges such as limited in-bore space, prolonged procedure times, and radiation exposure. Robotic assistance offers a promising solution by improving needle trajectory accuracy, reducing radiation exposure, and enabling real-time adjustments. In our previous work, we introduced a robotic platform designed for accurate needle insertion within the confined CT bore. However, its performance in clinical settings is restricted by limited dexterity and a constrained workspace. In this study, we present an 11-degree-of-freedom (DOF) robotic system that integrates a 6-DOF robotic base with an improved 5-DOF cable-driven end-effector, yielding a significantly expanded workspace and enhanced dexterity. To leverage the hyper-redundant degrees of freedom, we introduce a weighted inverse kinematics controller, along with a null-space control strategy to optimize maneuverability and dexterity. By using a task-oriented weight matrix as a hyperparameter, the system provides a two-stage priority scheme fit for both large-scale movement and fine in-bore adjustments. In clinically relevant simulated scenarios, the system demonstrates a consistent 97% reachability rate across various human models. In addition, the task-oriented weight-matrix policy is extensively explored in five representative subtasks seen during needle biopsy through both simulation and real-world experiments, demonstrating superior tracking accuracy and enhanced manipulability for CT-guided procedures.
Figures & tables
Fig. 1 : Dexterous 11-DOF redundant robotic system executes needle insertion task in confined image bore.
Fig. 2 : Our dexterous 11-DOF redundant robotic system utilizes a 6-DOF robotic base, while a 5-DOF cable-driven end-effector provides precise needle positioning. A translate-to-rotate mechanism is used to measure the 11th joint. The end-effector’s joint positions are transmitted wirelessly.
system
11-DOF (our)
8-DOF (previous)
End
Effector
Weight
3.1 kg
4.6 kg
Base length
0.31 m
0.43 m
Main axis range
[−2π,2π]
[−π/2,π/2]
Joint data reading
Wireless
Wired
Insertion measure
Magn. encoder
Magn. tracker
TABLE I : System upgrades of the new 11-DOF system
Fig. 3 : The software architecture consists of high-level control and planning modules running on a desktop in ROS2 framework, while low-level real-time controllers operate on embedded microcontrollers.
Fig. 4 : Comparison of in-bore reachability between previous 8-DOF and new 11-DOF system. The color brightness on mesh surface shows reachable rate at each point. F is female and M is male, respectively. Larger patients result in less in-bore space and a more challenging environment.
Fig. 5 : Comparison of tracking error of 5 different trajectories with 3 weight policies. While the experiments were conducted in the real world, the top row presents simulation figures to illustrate the trajectory in the task. For the largest movement, reaching into the bore, W3 is able to translate the fastest since the 6-DOF robotic base is used. Meanwhile for in-bore manipulation, W1 is able to converge faster since the 5-DOF end-effector is used for the fine precision movements.
Trajectory
Metric
Weight Policy
W1
W2
W3
Reaching In-Bore
Tr,pos (s)
22.3
20.2
16.6
Ts,pos (s)
26.5
24.4
20.7
ess,pos (mm)
2.0
0.8
3.5
ess,ori ( ∘ )
0.4
0.1
0.5
In-Bore
TABLE II : Comparison of trajectory tracking performance under three different weight policies
Fig. 6 : Comparison of joint configurations between robots without (first row) and with (second row) null-space control. The end-effector deviates from the desired circle trajectory due to reaching a singularity without null-space control.
Fig. 7 : Comparison of Yoshikawa manipulability measure with and without null-space control.
Fig. 8 : Comparison of step response accuracy under different null-space control gain Kn and damping factor λ .
Fig. 9 : Demonstration of tele-operational control of our dexterous robot platform. The workflow consists of three stages: 1) large-scale motion to reach in-bore; 2) fine in-bore adjustment of needle pose; 3) precise needle insertion.
Hyper-redundant robots are well suited for confined-space manipulation due to their high dexterity, but safe operation in cluttered environments remains challenging. In addition, their slender structures often lead to uneven load distributions and nonuniform tracking errors along the body. To address these issues, this work proposes a weighted control barrier functions (W-CBFs) framework that enforces safety constraints while reducing tracking errors caused by uneven loading. The proposed controller was first evaluated on a circular path-following task under different obstacle configurations. With fixed weights, compared to the non-weighted method, the maximum reduction in root-mean-square (RMS) tracking error was 59.6% in simulation and 87.7% in physical experiments. An adaptive weighting strategy was then investigated based on the discrepancy between simulated and experimental performance under different mapping functions. The RMS errors were further reduced by 21.9% and 8.5%, respectively, although the error increases when obstacles were located close to the robot body. Finally, the robot was evaluated in a cleaning task requiring coverage of a rectangular area and compared with manual teleoperation. Although the controller was not explicitly optimized for area coverage, the autonomous strategy achieved comparable or better coverage performance while avoiding collisions with the surrounding frame, whereas collisions occurred during manual operation.
Zijian Cai, Kiwan Wong, Wenci Xin +3
Singapore-MIT Alliance for Research and Technology (SMART) Centre, Singapore · CSAIL, Massachusetts Institute of Technology, Cambridge, USA · School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore +1
We present a MR safe, master-slave robot manipulator for abdominal interventions in the MRI chamber. A human operated 2+1-DoF master controller manipulator transmits motion and force to a 2+1-DoF slave manipulator via fluid transmission. Jointly, a digital master controller provides multimodal control capability beyond common split axis or mode switchable hybrid human-digital controller configurations found in previous studies. High input impedance, low-leakage, elastomeric fluid actuators are delegated to remote angulation control. Low-friction graphite piston cylinders are delegated to needle insertion axis remote actuation given the sub-newton force transparency and sub-millimeter motion transmission over bedside fluid piping lengths. The device enables real-time MRI guided interventions allowing manual, digital, hybrid, and collaborative control modes. Collaborative tasks such as assisted tissue penetration, fault-driven virtual fixture, and motion compensation through feedback control are presented in this paper. Preliminary MR scanner results demonstrate manipulator functional viability for an in-vivo pig experiment in bedside, manual control mode configuration.
Omar Curiel, Jing-Yuan Huang, Po-Chih Chen +6
Department of Mechanical and Aerospace Engineering, Samueli School of Engineering, University of California, Los Angeles, CA 900095 USA · Horizon Surgical Systems Inc.,Malibu, CA 90265 · Department of Radiological Sciences, University of California, Los Angeles, CA 90095 USA
Ultrasound (US)-guided needle insertion is a critical yet challenging procedure due to dynamic imaging conditions and difficulties in needle visualization. Many methods have been proposed for automated needle insertion, but they often rely on hand-crafted pipelines with modular controllers, whose performance degrades in challenging cases. In this paper, a Vision-Language-Action (VLA) model is proposed for adaptive and automated US-guided needle insertion and tracking on a robotic ultrasound (RUS) system. This framework provides a unified approach to needle tracking and needle insertion control, enabling real-time, dynamically adaptive adjustment of insertion based on the obtained needle position and environment awareness. To achieve real-time and end-to-end tracking, a Cross-Depth Fusion (CDF) tracking head is proposed, integrating shallow positional and deep semantic features from the large-scale vision backbone. To adapt the pretrained vision backbone for tracking tasks, a Tracking-Conditioning (TraCon) register is introduced for parameter-efficient feature conditioning. After needle tracking, an uncertainty-aware control policy and an asynchronous VLA pipeline are presented for adaptive needle insertion control, ensuring timely decision-making for improved safety and outcomes. Extensive experiments on both needle tracking and insertion show that our method consistently outperforms state-of-the-art trackers and manual operation, achieving higher tracking accuracy, improved insertion success rates, and reduced procedure time, highlighting promising directions for RUS-based intelligent intervention.
Yuelin Zhang, Qingpeng Ding, Longxiang Tang +2
Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Hong Kong · Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Hong Kong · Shenzhen International Graduate School, Tsinghua University, China +1