Machine Learning and Control Systems Laboratory (MLCS)

연구실 소개

We strive to solve challenging societal problems by exploiting powerful modern computational and mathematical techniques from machine learning, systems and control, deep reinforcement learning, and Bayesian statistics. Our laboratory has been conducting, and continuously seeks for intriguing and multidisciplinary projects with scientists, medical clinicians, and engineers from robotics and automotive companies, including building artificial intelligence for inverse reinforcement learning of a driving style for autonomous driving; self-driving and manipulating mobile robots interacting with people in dynamical and uncertain environments; optimal path-planning for racing or electric vehicles; medical machine vision and data-driven, patient-specific models; and intent-analysis and physical human-robot interaction (pHRI) for gauging and rehabilitating human motor control systems. As results, our solutions are creative and successful with intellectual merits, and happen to synergistically combine recent advances in machine learning techniques such as group-equivarient models, deep learning, (deep) reinforcement learning, inverse reinforcement learning, and Gaussian process regression with engineering, mathematical, and/or statistical techniques such as convex optimization-based control synthesis, system identification, and Bayesian inferential methods.