Curriculum Vitae

• Depth Vision based Environment Recognition for Powered Exoskeleton: Developed a real-time stair localization and parameter estimation system for powered exoskeletons in Python using an RGBD camera, introducing adaptive parameterization to improve model accuracy and stability for enhanced mobility in diverse environments.
• Sim-to-Real Trajectory Generation using MPC: Utilized Model Predictive Control (MPC) and Python libraries Crocoddyl and Pinocchio to develop Sim-to-Real trajectory generation for a 12-DoF powered exoskeleton, enabling precise walking and stair-climbing trajectories and improving real-world adaptability.
• Cybathlon 2024: Secured first place in the Exoskeleton Race as a core motion control team member. Developed MPC algorithms for precise walking and adaptive gait control, ensuring stability and responsiveness on varied terrain.

• Designed and optimized component-based architecture utilizing Angular framework.
• Built reusable libraries and web apps with TypeScript and Angular.
• Developed next-generation Grid UI/UX for global utility customers through UI innovations and user experience prioritization.

• Worked on statistical planning and analysis of patient data from IoT devices and past medical records.
• Built APIs for retrieving data from MySQL database and serving it to the frontend (Python).
• Used machine learning tools on medical records to build personalized predictive models for lifestyle and chronic diseases (diabetes, hypertension, cardiovascular diseases).

GPA: 3.92/4.3 (96.2%)

Research Focus: Model Based Control, Learning Based Control, Depth Vision Based Environment Recognition

Currently on a two-year leave (April 2025 - Feb 2027) to pursue entrepreneurial ventures and startup activities.

GPA: 3.92/4.3 (96.2%)

Courses: Learning Based Control, Reinforcement Learning, Intro to Visual Intelligence, Mathematical Methods, Artificial Neural Network, Human Assistive Robotics.

Master's Thesis: Multi-Stage Optimization-Based Personalized Gait Generation in Powered Exoskeletons

This research introduces a multi-stage optimization-based framework for personalized gait generation in powered exoskeletons, addressing critical challenges in stability, safety, and user-specific adaptability. The framework integrates advanced optimization techniques, personalized modeling, and adaptive control, validated through simulations and experimental trials with the WalkON-F1 exoskeleton.

Score: 79.2%

Courses: Automatic Control, Robotics and Control, Kinematics of Machines, Dynamics of Machines, Modelling and Simulation, Operating System, Data Structures and Algorithms.

Published Papers

• Madhusudan Singh, Abhiraj Singh, Shiho Kim, "Blockchain Technology - A Game Changer for Securing IoT", in IEEE, 2018.

Preprints / Under Review

• Jongwon Kim, Abhiraj Singh, Jimin Youn, Hyeongjun Kim, Jeongsu Park, Jinsu Park, Kyoungchul Kong, "Optimization of Crutch-Free Walking for a Powered Exoskeleton Considering Human Adaptation", submitted to IFAC Mechatronics, 2025.

Manuscripts Ready for Submission

• Abhiraj Singh, Taeyeon Kim, Kyoungchul Kong, "Real-time Depth Vision-based Stair Localization and Parameter Estimation for Powered Exoskeletons".

Master's Thesis

Multi-Stage Optimization-Based Personalized Gait Generation in Powered Exoskeletons

This research introduces a multi-stage optimization-based framework for personalized gait generation in powered exoskeletons, addressing critical challenges in stability, safety, and user-specific adaptability. The framework integrates advanced optimization techniques, personalized modeling, and adaptive control, validated through simulations and experimental trials with the WalkON-F1 exoskeleton.