{"id":11094,"date":"2025-06-17T14:42:03","date_gmt":"2025-06-17T12:42:03","guid":{"rendered":"https:\/\/it-u.at\/?p=11094"},"modified":"2025-06-17T16:06:33","modified_gmt":"2025-06-17T14:06:33","slug":"exploring-human-machine-relationships-through-dance","status":"publish","type":"post","link":"https:\/\/it-u.at\/en\/news\/exploring-human-machine-relationships-through-dance\/","title":{"rendered":"Exploring Human-Machine Relationships Through Dance\u00a0"},"content":{"rendered":"\n

Christopher Frauenberger, Professor of Human-Computer Interaction at IT:U, and Christoph Hubatschke, PostDoc in the Human-Computer Interaction Research Group, are exploring AI research with and through contemporary dance. The research builds on the project DANCR, developed within the transdisciplinary research collective H.A.U.S. (Humanoids in Architecture and Urban Spaces), co-founded by Christoph Hubatschke.  <\/em><\/p>\n\n\n\n

Research at the Intersection of Dance and Technology<\/strong> <\/h3>\n\n\n\n

DANCR investigates the human-machine interface in contemporary dance through real-time collaboration between dancers and humanoid robots. It was developed by Oliver Sch\u00fcrer (TU Vienna), Eva Maria Kraft (dancer and choreographer), Christoph Hubatschke (IT:U) and Brigitte Krenn (Austrian Institute for Artificial Intelligence). At the heart of the project is the DANCR-tool, an AI-based system designed to explore improvisation and embodiment in dance. Trained using the movement data of individual dancers, the system allows the humanoid robot Pepper to perform alongside humans. In live performances, the robot mirrors and responds to the dancers, creating a dynamic interplay where dancers and machine co-create movement in real time. <\/p>\n\n\n\n

Bias by design, relationship by intention<\/strong> <\/h3>\n\n\n\n

While usually AI is trained with a large and diverse set of data, this project intentionally embraces a bias: the AI is deliberately trained on the highly specific styles of individual artists. This approach nurtures a unique, evolving relationship between human and machine, emphasizing artistic collaboration over automation. <\/p>\n\n\n

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\n \"\"\u00a9Bruckner University<\/span> <\/div>\n
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\n \u201cUsually, AI is trained on large and diverse datasets in the hope to avoid bias, which is of course not possible. In our case, bias isn\u2019t just unavoidable, it\u2019s intentional and essential. Bias is the method. The robot is moved exclusively by the particular movement-set of the dancers involved.\u201d\u00a0 <\/blockquote>\n \n Christoph Hubatschke\u00a0\u00a0 <\/cite>\n <\/div>\n <\/div>\n <\/div>\n<\/div>\n\n\n

The DANCR-tool combines two AI systems: <\/p>\n\n\n\n