Software Engineer/Roboticist
Columbia University
I'm a recent graduate student working on artificial intelligence, embedded systems and HCI. My current research focus is applying the latest works from academia onto assistive rehabilitative orthotics.
Email: r.ho [at] columbia.edu
ReGlove is a low-cost vision-guided pneumatic glove that enables assistive orthoses using wrist-mounted cameras and edge computing, achieving 96.73% grasp accuracy and 82.71% success on object manipulation tasks—all for under $250 with commercial components.
A dynamics-driven pre-training method that learns manipulation-relevant representations from multi-modal human demonstrations (vision and touch) without action labels. By jointly learning inverse and forward dynamics and fine-tuning with few robot demonstrations, it significantly improves data efficiency, generalization, and robustness in contact-rich manipulation tasks compared to prior visual pre-training methods
Improves stroke rehabilitation by enabling prosthetic and exoskeleton hands to automatically select appropriate grasps using vision. Combines RGB images with depth maps to recognize five daily-use grasp types, with results showing depth cues compensate for limitations in muscle signals.
Built a quadruped robot from scratch, including mechanical design, electronics assembly, and software integration purely with laser-cutting to reduce build time and optimized for lightweight structure.
Developed a multi-modal sensor array for collecting human demonstration data, including RGB/depth cameras (Intel RealSense D435 and D405) mounted on the head and wrist, plus tactile sensing gloves. The ROS2-based system captures visual streams at 35Hz and converts depth data to point clouds for cross-embodiment pre-training.
Developed a lightweight computer vision pipeline using YOLO to detect solar installation pole tips from RGB images with high precision. The model demonstrates strong localization accuracy suitable for fast, on-device real-world applications.
Built 'CULib Study Spot,' a real-time occupancy tracking app for Columbia University Libraries that helps students locate available study spaces efficiently. The platform features live seat availability, quiet zone indicators, and floor suggestions, while providing librarians with an independent tool for managing capacity data.
A Columbia Business School political marketing analysis project investigating how candidates signal bipartisanship and elite credentials supervised by Professor Mohamed Hussein. Features LLM-based tweet classification and campaign website scraping to understand impacts on voter perception.