Prince Kwabena Appiah Boadu
I’m a systems engineer and ai/robotics researcher. I build autonomous systems and study the places where machine learning, software engineering, and design meet.
This site is my working archive: research notes, open-source systems, long-form essays, and manuscripts shaped by the questions I’m currently following.
At the moment, I’m teaching a practical course on robotics and continuing work across robot learning, perception, and infrastructure for intelligent systems.
Recent writing
Jul 31, 2026Book Review: How Minds Change - David McRaneyPsychologyJul 6, 2026Rigid Body MotionsRoboticsMay 17, 2026Building an Oware Engine and a Ladder of RL AgentsMachine LearningMay 7, 2026A Hybrid Computer Vision Pipeline for Real-Time Traffic Density EstimationMachine LearningApr 6, 2026Configuration Space of RobotsRoboticsMar 23, 2026Dynamic Traffic Shaping by Reinforcement Learning AgentsMachine Learning
Recent manuscripts
2026Seamless Deployment of Machine Learning Inference at the Far Edge: An Empirical AWS IoT Greengrass Architecture for Industrial Predictive MaintenanceEdge Computing · IIoT2026Real-Time Traffic Density Estimation: A Hybrid Computer Vision and Predictive Machine Learning PipelineComputer Vision2026Adaptive Network Traffic Shaping with Deep Reinforcement Learning: A PPO-Driven Token Bucket on ns-3Reinforcement Learning · Networking2026The Evolution of xDSL Technologies: Broadband Demands, Technical Principles, and Copper LimitationsNetworking2024A Comprehensive Survey of Side-Channel Attacks in IoT Devices: Techniques and CountermeasuresIoT Security
Selected work
2026Real-Time Traffic Density EstimationA two-stage pipeline for traffic monitoring. The first stage pairs an object detector with a spatial logic layer that filters detections against a precomputed road mask, so only on-road vehicles contribute to density measurements.2025Humanoid Balancing Policy with MjLabA stabilizing control policy for the 29-DOF Unitree G1 humanoid trained with PPO and a small Actor-Critic network (512 → 256 → 128). Built on the mjlab framework with MuJoCo Warp to simulate 4,096 parallel environments directly on the GPU, bypassing CPU bottlenecks.