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About

Sergio Casas is a leading researcher at the intersection of robotics, simulation, and mixed reality, with key contributions spanning motion cueing algorithms, autonomous driving, and robotic-assisted surgery. His work on motion cueing algorithms for vehicle simulators, including the highly cited "Towards a simulation-based tuning of motion cueing algorithms" (38 citations) and the comprehensive "Motion Cueing Algorithms: A Review" (22 citations), has advanced the realistic reproduction of accelerations within constrained workspaces, using innovative approaches like particle swarm optimization and AM-FM bi-modulated signals. In surgical training, Casas has pioneered mixed reality tools, such as the 2023 study on a new mixed reality tool for minimally invasive robotic-assisted surgery (23 citations), and developed methods for real-time tracking and stereo matching in surgical videos (22 citations), addressing the steep learning curve of RAS. His impact extends to autonomous driving with "Copilot4D: Learning Unsupervised World Models for Autonomous Driving via Discrete Diffusion" (7 citations) and "StrObe: Streaming Object Detection from LiDAR Packets" (8 citations), showcasing his versatility. Casas also created ROMOT, a robotic 3D movie theatre for driving safety awareness (23 citations), and explored multisensory experiences in VR and AR (9 citations). With over 150 citations across his top works, Casas continues to shape how simulation, robotics, and mixed reality enhance training, safety, and autonomy.