Bi-manual Stabilization of the Cervical Spine for Safe Physical Human-Robot Interaction in Rolling Maneuvers
Authors: Elizabeth Peiros, Moira Bohley, Lucas Yager, Derek Chen, Rebecca Fellbaum, Chris D'Ambrosia, Ananya Rao, Xiao Liang, Michael C. Yip
Organizations: Electrical and Computer Engineering Department, University of California, San Diego, La Jolla, CA 92093 USA · Mechanical and Aerospace Engineering Department, University of California, San Diego, La Jolla, CA 92093 USA · Columbia University Irving Medical Center, National Outdoor Leadership School Wilderness Medicine Institute
The neck, specifically the cervical spine, is a highly fragile and mobile part of the body with a high risk of catastrophic injury. Whether someone is injured on a battlefield, in a natural disaster, or in an athletic event, great care is always taken with the cervical spine when transporting, rolling, or moving the patient. In this work, we present an algorithm and adaptations for bi-manual robots to stabilize the cervical spine during rolling maneuvers. We investigate techniques to maximize grip strength within safety bounds and present a control approach for proper neck rotation. We found that with our approach, a bi-manual robot is capable of achieving human-level performance in maintaining target rotational alignments. The axial alignment for the robot with prediction was within .21 degrees of human novices.
Table I: Human model parameters: Neck Joint Limits
Frame
Attached to
Obtained from
Status
B
Robot base
—
Assumed Fixed
C
Camera
Robot FK
Constant
Mh , Mt
ArUco markers on head, torso
Marker detection
Measured
H , Tr
Head, torso biomechanics frames
Fixed offsets MhTH , MtTTr
Calibrated constants
L , R
Left, right end-effectors
Forward kinematics FK(q)
Measured
F0
Torso pose at episode start
Snapshot of BTTr at k=0
Constant per episode
Table II: Coordinate frames.
Figure 3: Phantom used for testing which features a F/T load cell and bio-mechanically representative neck joint.
Material
fy (N)
Pressure (kPa)
fr (N)
Ratio
Ecoflex 00-10
3.0
12.43
19.57
1.574
5.0
14.74
15.45
1.048
7.0
16.49
14.27
0.865
Ecoflex 00-20
3.0
12.43
18.72
1.506
5.0
14.74
18.83
1.277
7.0
16.49
19.86
1.204
Table III: Force–pressure ratios by material
Figure 7: (A) Simulated grasping and trajectory showing the head and floor collision boundaries. (B) Neck stabilization from the vantage point of the camera on the robot (C) Example neck-stabilizing sequence
Figure 8: Comparison of expert (n=21) and novice (n=56) humans to a humanoid robot with (n=12) and without (n=12) a predictive algorithm in a neck stabilization task. (A) Maximum axial rotation error of head relative to torso. (B) Max angular distance error of neck bending relative to center axial line of torso. (C) Max force exerted on neck during neck stabilization task.
Accurate head immobilization is critical for medical procedures such as neuroimaging and intracranial radiation therapy, where even minor motion can compromise precision. Traditional methods, such as rigid stereotactic frames and thermoplastic face masks, are invasive, uncomfortable for patients, and require a time-consuming setup. To overcome these limitations, a novel approach employs a 6-degree-of-freedom (6DoF) robotic head holder that is integrated with real-time optical motion tracking, enabling dynamic compensation for head movements without physical restraint. However, implementing this system requires a detailed biomechanical understanding of skull-cervical spine interactions to define safe and effective motion limits. In this study, we conduct a human volunteer investigation using a 6DoF Stewart platform robot to characterize biokinematic workspace of the head-neck system and establish foundational data for human-robot interaction in precision head stabilization.
Xinmin Liu, Alexander L. Van Slyke, Destinee Holloman +2
Department of Radiation Oncology, University of California at Los Angeles, CA · Department of Radiation Oncology, Yale New Haven Hospital, Trumbull, CT · University of Colorado, Anschutz Medical Campus, Aurora, CO +1
We investigate in-hand rolling manipulation using a multifingered robot hand, where each finger is compliant and equipped with a tactile fingertip providing contact location and wrench information. We derive the equations of motion for compliant quasistatic in-hand rolling manipulation and formulate a fingertip rolling manipulation controller for multiple fingers to achieve a desired object twist within a grasp. The contact mechanics are demonstrated in simulation and the controller is tested on an experimental robot system.
Huan Weng, Yifei Chen, Kevin M. Lynch
Center for Robotics and Biosystems and the Department of Mechanical Engineering, Northwestern University, Evanston, IL 60208 USA · Northwestern Institute on Complex Systems (NICO)
Dexterous object rotation is a sequential contact problem: each support, release, and re-contact decision must both produce the desired object motion, and prepare the hand configuration for continued rotation. Existing reinforcement learning methods discover such movement patterns through trial and error on specific robotic hand embodiments, without explicitly accounting for how each contact transition affects the hand's ability to sustain object rotation in subsequent steps. We introduce DexMani, a framework that transfers human demonstrations as contact-conditioned manipulability evolution. This prior captures how successful human contact transitions reshape the object-rotation directions available to the hand. DexMani then learns this manipulability evolution and uses it to guide downstream reinforcement learning, enabling rotation skills to be acquired across robot embodiments with distinct kinematics and active-contact configurations. Across the Shadow Hand, Allegro Hand, and XHand, DexMani achieves the highest success rates in every evaluated setting for both seen and unseen objects. DexMani reaches an average success rate of 57.5% on LEAP Hand, outperforming other baselines and producing smoother rotatory motions. Project site: https://dexmani.github.io
Xiaoyang Chen, Shengcheng Luo, Haoran Guo +4
Shanghai Jiao Tong University · ShanghaiTech University · Beijing Institute for General Artificial Intelligence (BIGAI) +1