Hahne, Fabian ; Prasad, Vignesh ; Kshirsagar, Alap ; Koert, Dorothea ; Stock-Homburg, Ruth ; Peters, Jan ; Chalvatzaki, Georgia (2024)
Transition State Clustering for Interaction Segmentation and Learning.
HRI ’24 Companion. Boulder, CO, USA (March 11–14, 2024)
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
Hidden Markov Models with an underlying Mixture of Gaussian structure have proven efective in learning Human-Robot Interactions from demonstrations for various interactive tasks via Gaussian Mixture Regression. However, a mismatch occurs when segmenting the interaction using only the observed state of the human compared to the joint state of the human and the robot. To enhance this underlying segmentation and subsequently the predictive abilities of such Gaussian Mixture-based approaches, we take a hierarchical approach by learning an additional mixture distribution on the states at the transition boundary. This helps prevent misclassifcations that usually occur in such states. We fnd that our framework improves the performance of the underlying Gaussian Mixture-based approach, which we evaluate on various interactive tasks such as handshaking and fistbumps.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2024 |
| Autor(en): | Hahne, Fabian ; Prasad, Vignesh ; Kshirsagar, Alap ; Koert, Dorothea ; Stock-Homburg, Ruth ; Peters, Jan ; Chalvatzaki, Georgia |
| Art des Eintrags: | Bibliographie |
| Titel: | Transition State Clustering for Interaction Segmentation and Learning |
| Sprache: | Englisch |
| Publikationsjahr: | 2024 |
| Ort: | Boulder, CO, USA |
| Veranstaltungstitel: | HRI ’24 Companion |
| Veranstaltungsort: | Boulder, CO, USA |
| Veranstaltungsdatum: | March 11–14, 2024 |
| Kurzbeschreibung (Abstract): | Hidden Markov Models with an underlying Mixture of Gaussian structure have proven efective in learning Human-Robot Interactions from demonstrations for various interactive tasks via Gaussian Mixture Regression. However, a mismatch occurs when segmenting the interaction using only the observed state of the human compared to the joint state of the human and the robot. To enhance this underlying segmentation and subsequently the predictive abilities of such Gaussian Mixture-based approaches, we take a hierarchical approach by learning an additional mixture distribution on the states at the transition boundary. This helps prevent misclassifcations that usually occur in such states. We fnd that our framework improves the performance of the underlying Gaussian Mixture-based approach, which we evaluate on various interactive tasks such as handshaking and fistbumps. |
| Fachbereich(e)/-gebiet(e): | 01 Fachbereich Rechts- und Wirtschaftswissenschaften 01 Fachbereich Rechts- und Wirtschaftswissenschaften > Betriebswirtschaftliche Fachgebiete 01 Fachbereich Rechts- und Wirtschaftswissenschaften > Betriebswirtschaftliche Fachgebiete > Fachgebiet Marketing & Personalmanagement |
| Hinterlegungsdatum: | 18 Mär 2024 13:05 |
| Letzte Änderung: | 18 Mär 2024 13:05 |
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