Whole Body

Whole-body research encompasses a broad range of applications, focusing on the integrated analysis and control of entire systems, from robotic bodies to human anatomy and even abstract representations like graphs. Current research emphasizes efficient algorithms for whole-body control in robotics, often employing model predictive control, Kalman filtering, and deep learning architectures like UNets and Transformers to address challenges in pose estimation, manipulation, and collision avoidance. These advancements have significant implications for improving robotic capabilities in diverse fields, enhancing medical image analysis for improved diagnostics, and advancing our understanding of complex systems through novel modeling techniques.