{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T23:35:23Z","timestamp":1761176123909,"version":"build-2065373602"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686318","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T00:00:00Z","timestamp":1761004800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,10,21]]},"abstract":"<jats:p>Vision-based Imitation Learning has achieved remarkable success in gripper manipulation, as ut eliminates the need for exhaustive programming. However, current methods depend on large, task-specific datasets or require replaying policies under identical training conditions, which limiting their scalability. Moreover, as grippers evolve into humanoid hands, the challenge of precise control grows substantially. In this paper, we propose a speech-driven controller for human-sized robotic hands that translates natural language commands directly into actuator controls, removing the need for task-specific video data and enabling a more natural human\u2013robot interaction. To achieve this, we first train a model to map observed human hand motion to actuator controls through a camera. This learned model forms the foundation for rapidly developing a library of motion primitives. Subsequently, we train an Autoregressive Stateful Neural Network to convert verbal instructions into sequences of those primitives, composing multi-step trajectory sequences. We validate our approach by integrating the controller into the open-source InMoov Hand-i2 project. This work lays the groundwork for scalable, adaptable controllers that can be fine-tuned to new tasks with minimal effort. We provide the supplementary material and the code on Github: https:\/\/github.com\/kochlisGit\/InmoovNet.<\/jats:p>","DOI":"10.3233\/faia250828","type":"book-chapter","created":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:43:24Z","timestamp":1761126204000},"source":"Crossref","is-referenced-by-count":0,"title":["Learning to InMoov: A Deep Learning Approach to Modeling Human Hand"],"prefix":"10.3233","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9431-6679","authenticated-orcid":false,"given":"Vasileios","family":"Kochliaridis","sequence":"first","affiliation":[{"name":"Aristotle University of Thessaloniki"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-3322-2013","authenticated-orcid":false,"given":"Chrysoula","family":"Moschou","sequence":"additional","affiliation":[{"name":"Aristotle University of Thessaloniki"}]},{"given":"Alexandra","family":"Dimitrakopoulou","sequence":"additional","affiliation":[{"name":"Aristotle University of Thessaloniki"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3477-8825","authenticated-orcid":false,"given":"Vlahavas","family":"Ioannis","sequence":"additional","affiliation":[{"name":"Aristotle University of Thessaloniki"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","ECAI 2025"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA250828","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:43:24Z","timestamp":1761126204000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA250828"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,21]]},"ISBN":["9781643686318"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia250828","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,21]]}}}