Chenghua Lu received the B.S. degree in Mechanical Engineering from Northeastern University, Shenyang, China, in 2017, and the M.S. degree in Mechanical Manufacturing and Automation from the University of Chinese Academy of Sciences, Beijing, China, in 2021. She is currently working toward the Ph.D. degree majoring in Engineering Mathematics with the School of Mathematics Engineering and Technology and Bristol Robotics Laboratory, University of Bristol, Bristol, UK. Her research interests include tactile sensing and soft robotics. Kailuan T ang received a B.S. degree in Communication Engineering from the Southern University of Science and Technology (SUSTech), Shenzhen, China in 2017. He is currently working towards a Ph.D. degree majoring in Mechanics with the School of Mechatronics Engineering, Harbin Institute of Technology.

--Safe navigation of steerable and robotic catheters in the cerebral vasculature requires awareness of the catheter's shape and pose. Currently, a significant perception burden is placed on interventionalists to mentally reconstruct and predict catheter motions from biplane fluoroscopy images. Efforts to track these catheters are limited to planar segmentation or bulky sensing instrumentation, which are incompatible with microcatheters used in neurointervention. In this work, a catheter is equipped with custom radiopaque markers arranged to enable simultaneous shape and pose estimation under biplane fluoroscopy. A design measure is proposed to guide the arrangement of these markers to minimize sensitivity to marker tracking uncertainty. Endovascular neurosurgery is a rapidly growing domain which enables treatment of cerebrovascular disease with minimally-invasive approaches. Among the most common endovascular neurointerventions include aneurysm coiling and mechanical thrombectomy (MT), which has become the gold standard for treating strokes caused by large vessel occlusions (L VOs).

Fiducial marker positions in projection image of cone-beam computed tomography (CBCT) scans have been studied to evaluate daily residual motion during breath-hold radiation therapy. Fiducial marker migration posed challenges in accurately locating markers, prompting the development of a novel algorithm that reconstructs volumetric probability maps of marker locations from filtered gradient maps of projections. This guides the development of a Python-based algorithm to detect fiducial markers in projection images using Meta AI's Segment Anything Model 2 (SAM 2). Retrospective data from a pancreatic cancer patient with two fiducial markers were analyzed. The three-dimensional (3D) marker positions from simulation computed tomography (CT) were compared to those reconstructed from CBCT images, revealing a decrease in relative distances between markers over time. Fiducial markers were successfully detected in 2777 out of 2786 projection frames. The average standard deviation of superior-inferior (SI) marker positions was 0.56 mm per breath-hold, with differences in average SI positions between two breath-holds in the same scan reaching up to 5.2 mm, and a gap of up to 7.3 mm between the end of the first and beginning of the second breath-hold. 3D marker positions were calculated using projection positions and confirmed marker migration. This method effectively calculates marker probability volume and enables accurate fiducial marker tracking during treatment without requiring any specialized equipment, additional radiation doses, or manual initialization and labeling. It has significant potential for automatically assessing daily residual motion to adjust planning margins, functioning as an adaptive radiation therapy tool.

In the past ten years, deep learning (DL) has profoundly influenced the development of computer vision algorithms, enhancing their performance and robustness in various applications like image classification, segmentation, and object tracking. This momentum has carried into spacecraft pose estimation, where DL-based methods have begun to surpass traditional feature-engineering techniques as reported in the literature [1-3], corner and marker detection algorithms such as Shi-Tomasi, Hough Transform methods [4, 5]. CNNs have the edge over feature-based methods primarily due to their enhanced robustness against poor lighting conditions and their streamlined computational demands. However, when it comes to space imagery, the scenario changes due to the distinct challenges such as high contrast, low signal-to-noise ratio, and inferior sensor resolution, which can diminish accuracy.

Real-time optical Motion Capture (MoCap) systems have not benefited from the advances in modern data-driven modeling. In this work we apply machine learning to solve noisy unstructured marker estimates in real-time and deliver robust marker-based MoCap even when using sparse affordable sensors. To achieve this we focus on a number of challenges related to model training, namely the sourcing of training data and their long-tailed distribution. Leveraging representation learning we design a technique for imbalanced regression that requires no additional data or labels and improves the performance of our model in rare and challenging poses. By relying on a unified representation, we show that training such a model is not bound to high-end MoCap training data acquisition, and exploit the advances in marker-less MoCap to acquire the necessary data. Finally, we take a step towards richer and affordable MoCap by adapting a body model-based inverse kinematics solution to account for measurement and inference uncertainty, further improving performance and robustness. Project page: https://moverseai.github.io/noise-tail

Although manipulation of known objects is a well-studied field, handling deformable or small, fragile objects with II. BACKGROUND AND RELATED WORK human-level skill is a challenge. Behaviors for robotic hands A. Motion Capture of Human Hands can be generated through various approaches, e.g., planning, Capturing human hand motions as fully articulated 3D reinforcement learning, or imitation learning. We are interested hand poses is demanding due to the dexterity of hands and in leveraging intuitive human knowledge to generate high angular velocities. Nevertheless, the task is well studied data for imitation learning with a complex hand. Dataset and there are numerous solutions, including optical, nonoptical, generation is difficult in this case.