1 citations · 1 across the 2 of their papers we have counts for
7 papers
VERAGMIL: Virtual Environment for Scooping Granular Foods with Imitation Learning Models
Amanuel Ergogo, Diego Dall'Alba, Przemyslaw Korzeniowski
Robot-Assisted Feeding (RAF) systems are essential for assisting individuals with disabilities or motor impairments in eating tasks. Manipulating granular food items, such as rice…
Open-H-Embodiment: A Large-Scale Dataset for Enabling Foundation Models in Medical Robotics
Open-H-Embodiment Consortium, :, Nigel Nelson +213
Autonomous medical robots hold promise to improve patient outcomes, reduce provider workload, democratize access to care, and enable superhuman precision. However, autonomous medic…
GEPAR3D: Geometry Prior-Assisted Learning for 3D Tooth Segmentation
Tomasz SzczepaÅski, Szymon PÅotka, Michal K. Grzeszczyk +5
Tooth segmentation in Cone-Beam Computed Tomography (CBCT) remains challenging, especially for fine structures like root apices, which is critical for assessing root resorption in…
PR-ENDO: Physically Based Relightable Gaussian Splatting for Endoscopy
Joanna Kaleta, Weronika Smolak-Dyżewska, Dawid Malarz +3
Endoluminal endoscopic procedures are essential for diagnosing colorectal cancer and other severe conditions in the digestive tract, urogenital system, and airways. 3D reconstructi…
FF-SRL: High Performance GPU-Based Surgical Simulation For Robot Learning
Diego Dall'Alba, MichaÅ NaskrÄt, Sabina Kaminska +1
Robotic surgery is a rapidly developing field that can greatly benefit from the automation of surgical tasks. However, training techniques such as Reinforcement Learning (RL) requi…
SimuScope: Realistic Endoscopic Synthetic Dataset Generation through Surgical Simulation and Diffusion Models
Sabina Martyniak, Joanna Kaleta, Diego Dall'Alba +3
Computer-assisted surgical (CAS) systems enhance surgical execution and outcomes by providing advanced support to surgeons. These systems often rely on deep learning models trained…