{"id":5132,"date":"2026-05-13T15:08:05","date_gmt":"2026-05-13T19:08:05","guid":{"rendered":"https:\/\/oge.mit.edu\/msrp\/?post_type=profiles&#038;p=5132"},"modified":"2026-08-11T17:00:14","modified_gmt":"2026-08-11T21:00:14","slug":"olumide-ogunmakinwa-2","status":"publish","type":"profiles","link":"https:\/\/oge.mit.edu\/msrp\/profiles\/olumide-ogunmakinwa-2\/","title":{"rendered":"Olumide Ogunmakinwa"},"content":{"rendered":"<div class=\"wp-block-image\">\n<figure class=\"alignleft size-full is-resized\"><img loading=\"lazy\" decoding=\"async\" width=\"400\" height=\"599\" src=\"https:\/\/oge.mit.edu\/msrp\/wp-content\/uploads\/sites\/2\/2026\/05\/Ogunmakinwa-Olumide.jpg\" alt=\"by Corban Swain\" class=\"wp-image-5621\" style=\"aspect-ratio:1;object-fit:cover;width:200px;height:auto\" srcset=\"https:\/\/oge.mit.edu\/msrp\/wp-content\/uploads\/sites\/2\/2026\/05\/Ogunmakinwa-Olumide.jpg 400w, https:\/\/oge.mit.edu\/msrp\/wp-content\/uploads\/sites\/2\/2026\/05\/Ogunmakinwa-Olumide-200x300.jpg 200w\" sizes=\"auto, (max-width: 400px) 100vw, 400px\" \/><\/figure>\n<\/div>\n\n\n<div class=\"wp-block-group\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\">\n<p class=\"wp-block-paragraph\"><strong>Howard University<\/strong><br>Faculty Advisor: Prof. Paul Liang<br>Research Supervisor: Ray Song<br>Department: Electrical Engineering and Computer Science<\/p>\n<\/div><\/div>\n\n\n\n<div style=\"height:0px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<h4 class=\"wp-block-heading\"><strong>Biography<\/strong><\/h4>\n\n\n\n<p class=\"wp-block-paragraph\">Olumide T. Ogunmakinwa is a rising junior and Karsh STEM Scholar at Howard<br>University, pursuing a bachelor&#8217;s in Computer Engineering. His research interests focus on<br>computer architecture, VLSI design, and the hardware foundations that enable and constrain<br>intelligent computing systems. As a Karsh STEM Scholar, Olumide is passionate about<br>expanding access in STEM fields. He serves as a role model for students aspiring to pursue<br>research and graduate study. He plans to pursue a Ph.D. in Computer Engineering to advance<br>computing technologies from the ground up, believing that meaningful innovation requires<br>understanding systems at their most fundamental level. Outside of his academic pursuits and<br>community involvement, Olumide enjoys gaming and exploring superhero narratives, which<br>continue to inspire his creative approach to problem-solving in the technology field.<\/p>\n\n\n\n<p class=\"has-text-align-center wp-block-paragraph\"><br><strong>From Human Touch to Robot Hands: An Agentic Pipeline for Processing Multimodal<br>Tactile Manipulation Data<br>Olumide Ogunmakinwa1, Y. Ray Song2, and Paul P. Liang2<\/strong><br>1Department of Electrical Engineering and Computer Science, Howard University<br>2Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology<\/p>\n\n\n\n<p class=\"has-text-align-center wp-block-paragraph\"><br>Robots still handle objects far less capably than people, largely because most robot learning uses<br>vision alone and never sees the forces a hand applies. Wearable tactile gloves can now record those<br>forces during ordinary activity, and OpenTouch pairs them with hand pose and first-person video<br>across hundreds of everyday manipulation sessions. Raw recordings are not training data. Each session<br>must be checked for quality, synchronized, and segmented into individual moments of contact,<br>work that does not scale by hand. We present an automated pipeline of specialized software agents<br>that converts raw multimodal recordings into structured, labeled contact events without manual review.<br>The agents audit recording quality and timing, detect each contact from the glove&#8217;s 256 pressure<br>points, mark its onset, peak, and release, and pair it with hand shape and wrist motion. Across<br>139 sessions the pipeline has produced over 5,000 contact events, and exposed a persistent sensor<br>bias in one glove that was suppressing contact detection, now corrected automatically. Ongoing work<br>extends the pipeline with automated visual annotation of contacts and a scoring method for identifying<br>which tactile scenarios are most informative for robot training, a step toward robots that learn<br>manipulation from human touch.<\/p>\n","protected":false},"featured_media":5437,"template":"","profile_category":[25],"class_list":["post-5132","profiles","type-profiles","status-publish","has-post-thumbnail","hentry","profile_category-2026-interns"],"acf":[],"_links":{"self":[{"href":"https:\/\/oge.mit.edu\/msrp\/wp-json\/wp\/v2\/profiles\/5132","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/oge.mit.edu\/msrp\/wp-json\/wp\/v2\/profiles"}],"about":[{"href":"https:\/\/oge.mit.edu\/msrp\/wp-json\/wp\/v2\/types\/profiles"}],"version-history":[{"count":3,"href":"https:\/\/oge.mit.edu\/msrp\/wp-json\/wp\/v2\/profiles\/5132\/revisions"}],"predecessor-version":[{"id":5737,"href":"https:\/\/oge.mit.edu\/msrp\/wp-json\/wp\/v2\/profiles\/5132\/revisions\/5737"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/oge.mit.edu\/msrp\/wp-json\/wp\/v2\/media\/5437"}],"wp:attachment":[{"href":"https:\/\/oge.mit.edu\/msrp\/wp-json\/wp\/v2\/media?parent=5132"}],"wp:term":[{"taxonomy":"profile_category","embeddable":true,"href":"https:\/\/oge.mit.edu\/msrp\/wp-json\/wp\/v2\/profile_category?post=5132"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}