Max Robotics — Global Service Robot Directory + US Certification

About

Wojciech Samek is a leading researcher at the intersection of machine learning, medical robotics, and multimodal data fusion. His work primarily focuses on developing deep learning architectures—particularly convolutional and recurrent neural networks—to solve critical challenges in surgical robotics and biomedical signal processing. A major contribution is his pioneering approach to sensorless force estimation in robot-assisted minimally invasive surgery, where he demonstrated that neural networks can accurately estimate interaction forces from visual data alone, eliminating the need for physical force sensors. His 2019 paper on recurrent convolutional neural networks for this task has garnered 93 citations, while his earlier 2015 work on multivariate machine learning methods for fusing multimodal functional neuroimaging data remains highly influential with 95 citations. Samek has also advanced vision-based sensor substitution, enabling 3D position and velocity estimation from monocular video. His research addresses a fundamental technological bottleneck in surgical robotics—providing haptic feedback without compromising instrument miniaturization. Through his innovative use of semi-supervised and deep learning models, Samek continues to push the boundaries of what is possible in autonomous and assistive surgical systems, making surgery safer and more precise.