Machine Learning and Control Systems Laboratory
Autonomous driving via marriage between deep learning and control theory:
We are working on a three year project entitled by “Autonomous driving control systems utilizing deep-learning based situation awareness and its uncertainty”, funded by National Research Foundation of Korea (NRF) (PI Choi). In this project, we study a novel deep learning model that estimates various parameters required for the autonomous driving control and their uncertainties as well in real time. Finally, we develop autonomous driving control strategies that can exploit estimated parameters and their uncertainties in order to guarantee the safety and performance of the autonomous vehicle.Inverse optimal control (IOC) and/or inverse reinforcment learning (IRL):
Experts' demonstration data can be used to learn reward functions in order to efficiently build artificial intelligence via inverse optimal control or inverse reinforcment. We are currently applying IRL to build AI for dual armed mobile robots to interact with human workers in dynamically uncertain environments.
Inverse optimal control (IOC) and/or inverse reinforcment learning (IRL):
Experts' demonstration data can be used to learn reward functions in order to efficiently build artificial intelligence via inverse optimal control or inverse reinforcment. We are currently applying IRL to build AI for dual armed mobile robots to interact with human workers in dynamically uncertain environments.
A MLCS team won the first place in BMW/Yonsei Student Research Competition 2018
Sponsored by College of Engineering, Yonsei University and by BMW Korea, the award ceremony was held on April 16, 2018 at the BMW Driving Center in Songdo, Incheon. Six teams from Yonsei University selected through the first round screening in early January announced their research results for about 3 months. Among them, "Autonomous ability to cope with risky situation via the awareness and reinforcement learning of vehicle surroundings" A research team of the school of mechanical engineering was awarded the First Prize. This MLCS team, composed of undergraduate students Sung-Won Lee, Jung-Hoon Lee, Sung-Ha Woo and graduate students Myunghoe Kim and Jaehyun Lim, researched under the guidance of Professor Jongeun Choi and won 5 million won. The last three months of the tournament ended at the end of the awards ceremony, but it will be further expanded in a variety of ways as a model of a research contest where colleges and industry labs work together in the future.AI service robots for VR theme parks:
The goal of the project is to develop an artificial intelligence based service robot for VR theme parks. The service robot can give cheering, encouragement, explanation, guidance, etc. according to the situations to make the customer happy. The service robot determines the current situation and recognizes features of the customer to provide customer-specific services based on vision, game status data, etc. using various machine learning techniques.
Gas classification using gas sensor array with machine learning and deep learning:
Our goal is to develop a classification framework that classifies unknown gas mixtures measured by a gas sensor array with modern machine learning (e.g., deep learning) techniques. reaction data are transferred to image, and used to train Convolution Neural Networks(CNN). We can solve the lack of data problem by data augmentation method.
Prediction and adaptive sampling for mobile sensor networks:
We have developed spatio-temporal prediction and adaptive sampling algorithms for mobile sensor networks. Mobile sensing agents need to locally learn the uncertain environment collaboratively with neighboring agents to achieve a global goal such as exploration and environmental monitoring. We are developing environmental adaptive sampling algorithms for mobile sensor networks to predict scalar fields of interest using nonparametric approaches raning from Gaussian processes, Gaussian Markov random fields, and kernel regression. This project has been funded by an NSF CAREER Award. A collection of successful outcomes will be disseminated as a SpringerBrief: “Bayesian Prediction and Adaptive Sampling Algorithms for Mobile Sensor Networks,” by Xu, Choi, Dass, and Maiti.
Prediction and adaptive sampling for mobile sensor networks:
We have developed spatio-temporal prediction and adaptive sampling algorithms for mobile sensor networks. Mobile sensing agents need to locally learn the uncertain environment collaboratively with neighboring agents to achieve a global goal such as exploration and environmental monitoring. We are developing environmental adaptive sampling algorithms for mobile sensor networks to predict scalar fields of interest using nonparametric approaches raning from Gaussian processes, Gaussian Markov random fields, and kernel regression. This project has been funded by an NSF CAREER Award. A collection of successful outcomes will be disseminated as a SpringerBrief: “Bayesian Prediction and Adaptive Sampling Algorithms for Mobile Sensor Networks,” by Xu, Choi, Dass, and Maiti.
Prediction and adaptive sampling for mobile sensor networks:
We have developed spatio-temporal prediction and adaptive sampling algorithms for mobile sensor networks. Mobile sensing agents need to locally learn the uncertain environment collaboratively with neighboring agents to achieve a global goal such as exploration and environmental monitoring. We are developing environmental adaptive sampling algorithms for mobile sensor networks to predict scalar fields of interest using nonparametric approaches raning from Gaussian processes, Gaussian Markov random fields, and kernel regression. This project has been funded by an NSF CAREER Award. A collection of successful outcomes will be disseminated as a SpringerBrief: “Bayesian Prediction and Adaptive Sampling Algorithms for Mobile Sensor Networks,” by Xu, Choi, Dass, and Maiti.
Prediction and adaptive sampling for mobile sensor networks:
We have developed spatio-temporal prediction and adaptive sampling algorithms for mobile sensor networks. Mobile sensing agents need to locally learn the uncertain environment collaboratively with neighboring agents to achieve a global goal such as exploration and environmental monitoring. We are developing environmental adaptive sampling algorithms for mobile sensor networks to predict scalar fields of interest using nonparametric approaches raning from Gaussian processes, Gaussian Markov random fields, and kernel regression. This project has been funded by an NSF CAREER Award. A collection of successful outcomes will be disseminated as a SpringerBrief: “Bayesian Prediction and Adaptive Sampling Algorithms for Mobile Sensor Networks,” by Xu, Choi, Dass, and Maiti.
Prediction and adaptive sampling for mobile sensor networks:
We have developed spatio-temporal prediction and adaptive sampling algorithms for mobile sensor networks. Mobile sensing agents need to locally learn the uncertain environment collaboratively with neighboring agents to achieve a global goal such as exploration and environmental monitoring. We are developing environmental adaptive sampling algorithms for mobile sensor networks to predict scalar fields of interest using nonparametric approaches raning from Gaussian processes, Gaussian Markov random fields, and kernel regression. This project has been funded by an NSF CAREER Award. A collection of successful outcomes will be disseminated as a SpringerBrief: “Bayesian Prediction and Adaptive Sampling Algorithms for Mobile Sensor Networks,” by Xu, Choi, Dass, and Maiti.
Modeling, assessment, and rehabilitation of human motor control systems:
The goal of the project is to apply concepts from systems and control theory to model and analyze the human motor control systems in order to generate useful information for treatments and interventions. Human-robot physical interaction is used to assess the motor control of the subject. The intent of the estimated motor control is analyzed by solving the formulated inverse optimal control problem. This project has been funded by an NIH center grant, “Systems Science Center for Musculoskeletal CAM Therapies.” See more information at Center for Orthopedic Research.
Modeling, assessment, and rehabilitation of human motor control systems:
The goal of the project is to apply concepts from systems and control theory to model and analyze the human motor control systems in order to generate useful information for treatments and interventions. Human-robot physical interaction is used to assess the motor control of the subject. The intent of the estimated motor control is analyzed by solving the formulated inverse optimal control problem. This project has been funded by an NIH center grant, “Systems Science Center for Musculoskeletal CAM Therapies.” See more information at Center for Orthopedic Research.
Modeling, assessment, and rehabilitation of human motor control systems:
The goal of the project is to apply concepts from systems and control theory to model and analyze the human motor control systems in order to generate useful information for treatments and interventions. Human-robot physical interaction is used to assess the motor control of the subject. The intent of the estimated motor control is analyzed by solving the formulated inverse optimal control problem. This project has been funded by an NIH center grant, “Systems Science Center for Musculoskeletal CAM Therapies.” See more information at Center for Orthopedic Research.
Modeling, assessment, and rehabilitation of human motor control systems:
The goal of the project is to apply concepts from systems and control theory to model and analyze the human motor control systems in order to generate useful information for treatments and interventions. Human-robot physical interaction is used to assess the motor control of the subject. The intent of the estimated motor control is analyzed by solving the formulated inverse optimal control problem. This project has been funded by an NIH center grant, “Systems Science Center for Musculoskeletal CAM Therapies.” See more information at Center for Orthopedic Research.
Patient-specific Bayesian calibration of computational models:
The goal of this project is to make prediction and gauge its uncertainty in the computer models for a patient (such as a growth and remodeling model) from observations and various uncertainties such as measurement noise, estimation errors, and biological variability. This research has been supported by an NIH R01 grant with a project “Growth and Remodeling Model of Abdominal Aortic Aneurysm: Toward Clinical Applications.”.bmp)
Patient-specific Bayesian calibration of computational models:
The goal of this project is to make prediction and gauge its uncertainty in the computer models for a patient (such as a growth and remodeling model) from observations and various uncertainties such as measurement noise, estimation errors, and biological variability. This research has been supported by an NIH R01 grant with a project “Growth and Remodeling Model of Abdominal Aortic Aneurysm: Toward Clinical Applications.”
Linear parameter varying (LPV) modeling and control:
Gain-scheduling controllers for various LPV systems with hard constraints have been designed based on the numerically efficient convex optimization or linear matrix inequality (LMI) technique. The simulation and experimental results demonstrate the effectiveness of the proposed approaches to energy-efficient engine systems. A collection of the works is published as a SpringerBrief: “Linear parameter-varying control for engineering applications,” by White, Zhu, and Choi.MLCS
We strive to solve challenging societal problems by exploiting powerful modern computational and mathematical techniques from machine learning, systems and control, system identification, Bayesian statistics, and sensor and data fusion. Our laboratory has been conducting, and continuously seeks for intriguing and multidisciplinary projects with biologists, scientists, medical clinicians, and researchers from robotic and game industries, including building artificial intelligence for self-driving and manipulating mobile robots interacting with people in dynamical environments; Bayesian prediction and adaptive sampling algorithms for multiagent systems (e.g., unmanned surface vehicles, i.e., USVs; unmanned ariel vehicles, i.e., UAVs) interacting with uncertain random fields; data-driven, patient-specific calibration of computational 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, and happen to synergistically combine recent advances in machine learning techniques such as deep learning, inverse reinforcement learning, and Gaussian process regression with engineering, mathematical, and/or statistical techniques such as LMI-based control synthesis, system identification, and Bayesian inferential methods.
NEWS
- Click to see our 20 new publications.
- We are pleased to inform that Special Issue "Deep Learning Based Sensing Technologies for Autonomous Vehicles" in Sensors has been up in the flowing link. Profesor Choi is co-organizing this SI together with Prof. Joon-Sang Park from Computer Engineering, Hongik Univ. and Prof. Kyougu Lee from Convergence Science, SNU, and pleae feel free to submit papers to this SI. a new special issue
- Professor Choi served (or serves) Journal Associate Editors for
- IEEE Robotics and Automation Letters. Review timeline is given here. Please submit your papers: RAS Conference/Journal Management System.
- ASME Journal of Dynamic Systems, Measurement and Control. Please submit your papers: ASME Journal Management System.
- International Journal of Precision Engineering and Manufacturing. Please submit your papers: IJPEM Journal Management System.
- A MLCS team won the first place in BMW/Yonsei student research competition 2018. Many congrats!
- We started a new project with a Yonsei team about "Multi-modal haptic interface laboratory" supported by National Research Foundation, South Korea, PI: Prof. Jongbaeg Kim; Co-PI: Prof. Jongeun Choi; Co-PI: Prof. Hyunseok Yang; Co-PI: Youngcheol Chae, 2018-2020.
- Four new graduate studuents joined MLCS in 2018!
- We are working on a three year project entitled by "Autonomous driving control systems utilizing deep-learning based situation awareness and its uncertainty", funded by National Research Foundation of Korea (NRF) (PI Choi).
- We started a five year project (2017-2022) entitled by "Development of VOCs sensor array based on nano-hybrid materials", funded by Ministry of Science and ICT collaborating with Professor Chun (PI) in Yonsei Mechanical Engineering, along with researchers from Kaist, Kookmin University, and Chemical Biomolecular Engineering of Yonsei University.
- We are working on a two year project entitled by "Development of an artificial intelligence based service robot for VR theme parks", funded by Korea Institute for Advancement of Technology collaborating with Jong Chan Lee (PI), CEO of Motion Device Co., Ltd., and Byung Guk Choi, director of IPWAY Co., Ltd.
- We are always seeking for talented adn highly motivated researchers (graduate students, postdocs, and undergradate interns). If you are interested in participating in research and collaboration, please feel free to contact Professor Choi.
- Professor Choi now teaching in School of Mechanical Engineering at Yonsei University from Fall 2016.
- Five graduate students joined us from 2017!
- We are starting a five year project (2017-2021) entitled by "Driving and manipulation intelligence based on deep learning and inverse reinforcement learning for dual arm mobile robot", funded by Ministy of Trade, Industry and Energy collaborating with Professor Hyunseok Yang (PI) in Mechanical Engineering, and Professor Seon Joo Kim in Computer Science.
