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To address these limitations, this study introduces a novel real\u2010time human\u2010robot interaction framework\u2010\n                    <jats:italic>AI\u2010RTGM<\/jats:italic>\n                    (Artificial Intelligence\u2010based Real\u2010Time Gesture Mapping)\u2010that unifies gesture recognition, trajectory generation, and robot control within a single adaptive pipeline. Unlike traditional models that depend on pre\u2010scripted commands or sensor\u2010bound gestures,\n                    <jats:italic>AI\u2010RTGM<\/jats:italic>\n                    integrates a CNN\u2010based vision model for continuous gesture tracking (GESTID) with a dynamic trajectory generation and execution module (TAGEM) capable of producing smooth, coordinated 7\u2010DoF motions in real time. The framework facilitates bidirectional communication between gesture perception and robotic actuation via ROS and\n                    <jats:styled-content>libfranka<\/jats:styled-content>\n                    , achieving an end\u2010to\u2010end latency below 70\u2009ms. Experimental validation on tasks such as pick\u2010and\u2010place and surface cleaning demonstrates precise, stable, and compliant motion replication directly driven by human intent. The proposed\n                    <jats:italic>AI\u2010RTGM<\/jats:italic>\n                    model fundamentally differs from existing approaches by integrating real\u2010time gesture perception with continuous trajectory\u2010level control, enabling a more natural, intuitive, and scalable paradigm for human\u2010robot collaboration.\n                  <\/jats:p>","DOI":"10.1002\/cpe.70643","type":"journal-article","created":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T05:29:51Z","timestamp":1773466191000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Real\u2010Time HRI: Human Hand Gesture Mapping for 7\u2010DoF Robotic Arm Operation"],"prefix":"10.1002","volume":"38","author":[{"given":"Sajjad","family":"Hussain","sequence":"first","affiliation":[{"name":"University of Brighton  Brighton 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