Oluwatimilehin Owolabi

I'm an undergraduate Electrical Engineering student at Covenant University. I lead the Robotics Research Team for Google Developer Groups on Campus, where we work on reinforcement learning, vision-language-action models, and low-cost robotic systems. Previously, I worked with Dr. Daniel Omeiza at the Oxford Robotics Institute on autonomous driving and graph neural networks.

I'm passionate about teaching and mentorship. I currently teach machine learning to undergraduate and graduate women of APWEN. I am the president of the Association of Electrical and Information Engineering Students and I founded the AEIES student mentorship program to help students master engineering concepts from first principles.

My vision is to unify machine learning, control theory, and cognitive science to build embodied AI systems that think, remember, and learn continuously.

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Research

I'm currently interested in meta-learning for self-improving policies, and learning-based model predictive control for mobility and manipulation in legged and aerial robots. That is, until I understand how to tackle my long-term vision of building truly cognitive systems.

Developed and tested a Fuzzy-PID controller for single- and double-tank liquid level systems, showing improved performance over conventional PID control in handling nonlinearity, uncertainty, and multivariable disturbances.

This work compares brute force and gradient descent optimization in neural networks, showing that brute force achieves better accuracy and lower memory usage, while gradient descent offers faster convergence—highlighting the potential of hybrid methods for more adaptable and efficient learning.

Developed a hybrid radiological image enhancement technique combining unsharp masking, logarithmic transformation, and adaptive histogram equalization, which outperforms CLAHE and Wavelet-based methods in visual quality metrics—offering a more standardized, accurate, and cost-effective solution for medical imaging systems.

Developed a smart home automation algorithm that combines appliance scheduling with real-time environmental sensing to optimize electricity use, achieving up to 68% energy savings in simulations—offering a scalable solution for reducing residential energy consumption and promoting smart grid integration.

Projects

Some of the projects I'm very passionate about.

Adaptive, optimal, and learning-based control for quadrotor swarms using gym-pybullet-drones.

Currently implementing Gaussian Process Model Predictive Control.

A low-cost, 6-DOF 3D-printed robotic arm designed to rival the performance of commercial manipulators like the WidowX 250 S—achieving similar reach, repeatability, and payload handling at just 2.5% of the cost. Fully open-source and built for accessible research, education, and prototyping.

Vision-based manipulation policy using the DeepMind Control Suite, MuJoCo Playground, and JAX to enable reliable pick-and-place performance in simulation. The system learns to detect and lift boxes autonomously, laying a foundation for scalable robotic manipulation with minimal supervision.

Simulates a TurtleBot in Gazebo with precise motion control via PID and robust localization using an Extended Kalman Filter, all built on the ROS 2 ecosystem.

CamAI is an AI-powered camera system that tracks people, recognizes actions in real time, and sends alerts during emergencies. Whether it's boosting safety in traffic zones, securing infrastructure, or enhancing agricultural efficiency, CamAI turns passive monitoring into intelligent response.

A U-Net-based deep learning app for segmenting brain tumors (necrotic, edema, enhancing) in 3D MRI scans. Uses FLAIR and T1CE images, with a FastAPI backend and Streamlit frontend for easy interaction and visualization.

A deep learning project that uses a U-Net model to segment clothing items from images of people. Whether it's shirts, pants, or mystery fashion choices, ClothSegNet finds the boundaries so your model doesn't have to guess where the pants end and the fashion crimes begin.

Miscellanea

Website template from here.