Machine Learning and Control Systems Laboratory
State reconstruction in a nonlinear vehicle suspension system using deep neural networks, Nonlinear Dynamics, 2021
In this work, we proposed and designed a long short-term memory (LSTM)-based neural network to estimate the velocity and position states of a full car’s suspension system using only online data streams from cheap inertial sensory measurements. Our approach shows that with only noisy accelerometer and gyroscope sensors, we can successfully reconstruct nearly perfect states in a full car’s nonlinear dynamic system using a well-trained LSTM-based neural network combined with the extended Kalman filter.
Virtual racing driver coaching project funded by Hyundai Motor Company
We aim to develop a virtual racing driver coach in order to coach human drivers to improve driving skills. The first goal is to develop a classification model which is able to evaluate the driving levels of racing drivers. The second objective is to develop a virtual racing driver that can be a reference to each level of the drivers and visually guide optimal vehicle control to the driver.
Nonaffine Helicopter Control Design and Implementation based on a Robust Explicit Nonlinear Model Predictive Control in IEEE Transactions on Control Systems Technology, 2021
To solve a control problem of the helicopter under model uncertainties and disturbance present environments, an explicit nonlinear model predictive control (ENMPC), a dynamic inversion, and an extended high-gain observer (EHGO) are combined in a multi-time-scale fashion. The multi-time scaled structure and the ENMPC provide the framework of the control design, the dynamic inversion deals with non-affine control inputs, and the EHGO estimates the unmeasured system states and uncertainties. The successful outdoor experiments with the proposed control implemented on autopilot hardware demonstrate the validity of our approach in the presence of model uncertainties and wind disturbances.
Multi-output Infinite Horizon Gaussian Processes in the Proceedings of 2021 International Conference on Robotics and Automation (ICRA 2021)
In this paper, we propose a multi-output infinite-horizon Gaussian process (MOIHGP) that generalizes the single-output IHGP to deal with multiple outputs for better prediction. Our approach allows us to consider correlations between multiple outputs for better prediction, even with occlusions in a Bayesian way. Finally, we successfully demonstrate the effectiveness of our approach by benchmark and experimental results. For simulated benchmark experiments with high noise levels, our approach reduced 16.6% of the averaged RMSE value achieved by the single-output IHGP.
Vision-based Uncertainty-aware Lane Keeping Strategy using Deep Reinforcement Learning in the Journal of Dynamic Systems, Measurement, and Control, 2021
In this work, for the uncertaintyaware lane keeping, we first propose a convolutional mixture density network (CMDN) model that estimates the lateral position error, the yaw angle error, and their corresponding uncertainties from the camera vision. We then establish a vision-based uncertainty-aware lane keeping strategy in which a high-level reinforcement learning policy hierarchically modulates the reference longitudinal speed as well as the low-level lateral control.
Automated cephalometric landmark detection with confidence regions using Bayesian convolutional neural networks, BMC Oral Health, 2020
In this paper, we propose the novel framework for locating cephalometric landmarks with confidence regions based on uncertainties using Bayesian Convolutional Neural Networks (BCNN). With Bayesian inference over iterative CNN model calculations, we can derive the confidence region (95%) of an identified landmark considering model uncertainty, and significantly higher the in-region accuracy. Given the uncertainty and confidence areas of the estimated location, clinicians are expected to determine whether the results of the framework are reliable and to make a more accurate diagnosis.
Prediction of Reward Functions for Deep Reinforcement Learning via Gaussian Process Regression in IEEE/ASME Transactions on Mechatronics and IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM) 2020
This new article proposes an efficient way to solve the inverse reinforcement learning problem based on the sparse Gaussian process (GP) prediction with l1-regularization only using a highly limited number of expert demonstrations. A GP model is proposed to be trained to predict a reward function using trajectory-reward pair data generated by deep reinforcement learning (RL) with different reward functions. The experimental results clearly show that the robots can clone the experts’ optimality in navigation trajectories avoiding obstacles using only with a very small number of expert demonstration data sets.
Efficient Sampling for Rapid Estimation of 3D Stiffness Distribution via Active Tactile Exploration in IEEE/ASME Transactions on Mechatronics and IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM) 2020
This paper proposes a novel efficient sampling strategy to rapidly estimate the distribution of stiffness over an inhomogeneous object with a highly limited number of sample points taken from the object surface. The main objective of this work is to improve the efficiency of the estimation process while producing an accurate estimate for both the overall distribution and some particular areas (i.e., high/low stiff areas). Physical experiments on a variety of inhomogeneous objects demonstrate the advantage of the proposed algorithm in comparison to a popular existing algorithm in terms of accuracy and estimation speed.
Customer-Sepcific Robotic Attendant for VR Simulators in IEEE Transactions on Automation Science and Engineering
This article proposes a new type of robotic attendant for VR simulators, which provides personalized high-quality services and automatically operates the VR simulators in VR theme parks. In addition, the robotic attendant improves customer satisfaction by providing personalized services based on the estimation of the customer’s age, gender, and game progress. Finally, we experimentally show that the personalized services provided by the robotic attendant improve customer satisfaction. The results of the satisfaction questionnaire and independent- samples t-test validate our proposed scheme.
Automated skeletal classification using lateral cephalogram based on AI in Journal of Dental Research:
This study aims to provide an accurate and robust skeletal diagnostic system by incorporating a convolutional neural network (CNN) into a one-step, end-to-end diagnostic system using lateral cephalograms. A multimodal CNN model was constructed based on 5,890 lateral cephalograms and demographic data as an input. The proposed system exhibited greater than 90% sensitivity, specificity, and accuracy for vertical and sagittal skeletal diagnosis. The proposed CNN incorporated system showed potential for skeletal orthodontic diagnosis without the need for intermediary landmarking procedures.Autonomous driving via marriage between deep learning and control theory: Unexpected Collision Avoidance Driving Strategy Using Deep Reinforcement Learning in IEEE Access
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.
Patient-Specific Prediction of Abdominal Aortic Aneurysm Expansion using Bayesian Calibration in IEEE journal of biomedical and health informatics
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.”
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.
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.
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.Vision
We strive to solve challenging societal problems by exploiting powerful modern computational and mathematical techniques from machine learning, systems and control, system identification, and Bayesian statistics. 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 and uncertain environments; Bayesian prediction and adaptive sampling algorithms for multiagent systems (e.g., unmanned ariel vehicles, i.e., UAVs) interacting with uncertain random fields; medical machine vision and 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 with intellectual merits, and happen to synergistically combine recent advances in machine learning techniques such as deep learning, 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.
NEWS
- MLCS's 20 new publications.
- Professor Choi serves
- as a Guest Editor with a two-year term (2021 and 2022) for IEEE/ASME TMECH/AIM Focused Section on Emerging Topics.
- as an Associate Editor for 2021 IEEE International Conference on Robotics and Automation.
- IEEE Robotics and Automation Letters. Review timeline is given here. RAS Conference/Journal Management System.
- ASME Journal of Dynamic Systems, Measurement and Control. ASME Journal Management System.
- International Journal of Precision Engineering and Manufacturing. IJPEM Journal Management System.
