Olumide Ogunmakinwa

Olumide Ogunmakinwa

Biography

Olumide T. Ogunmakinwa is a rising junior and Karsh STEM Scholar at Howard University, pursuing a bachelor’s in Computer Engineering. His research interests focus on computer architecture, VLSI design, and the hardware foundations that enable and constrain intelligent computing systems. As a Karsh STEM Scholar, Olumide is passionate about expanding access in STEM fields. He serves as a role model for students aspiring to pursue research and graduate study. He plans to pursue a Ph.D. in Computer Engineering to advance computing technologies from the ground up, believing that meaningful innovation requires understanding systems at their most fundamental level. Outside of his academic pursuits and community involvement, Olumide enjoys gaming and exploring superhero narratives, which continue to inspire his creative approach to problem-solving in the technology field.

From Human Touch to Robot Hands: An Agentic Pipeline for Processing Multimodal Tactile Manipulation Data
Olumide Ogunmakinwa¹, Y. Ray Song^2, and Paul P. Liang^2
1Department of Electrical Engineering and Computer Science, Howard University
²Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology

Robots still handle objects far less capably than people, largely because most robot learning uses vision alone and never sees the forces a hand applies. Wearable tactile gloves can now record those forces during ordinary activity, and OpenTouch pairs them with hand pose and first-person video across hundreds of everyday manipulation sessions. Raw recordings are not training data. Each session must be checked for quality, synchronized, and segmented into individual moments of contact, work that does not scale by hand. We present an automated pipeline of specialized software agents that converts raw multimodal recordings into structured, labeled contact events without manual review. The agents audit recording quality and timing, detect each contact from the glove’s 256 pressure points, mark its onset, peak, and release, and pair it with hand shape and wrist motion. Across 139 sessions the pipeline has produced over 5,000 contact events, and exposed a persistent sensor bias in one glove that was suppressing contact detection, now corrected automatically. Ongoing work extends the pipeline with automated visual annotation of contacts and a scoring method for identifying which tactile scenarios are most informative for robot training, a step toward robots that learn manipulation from human touch.