Humanoid robots have been successfully used in artistic research areas, and many works have studied and implemented systems for robotic dance. However, only few works take into account the human evaluation of these artistic outputs. This work makes a step in the direction of addressing the complex task of defining criteria for the evaluation of robotic dance performances. For this aim, in the context of a Master course on Fundamentals of Artificial Intelligence (AI), we have organized a challenge among our students and the winner is decided on the basis of a questionnaire we defined for robotic dance evaluation. In addition, we created a public dataset that maps the features of each choreography to the judgements provided by audience with different backgrounds on several evaluation targets. Then, we tested various Machine Learning models for predicting the audience evaluation, and we propose a choreography features importance analysis to help both human choreographers and AI algorithms to create dance performances with a major impact on the audience. We also suggest new directions for future interdisciplinary research
De Filippo A., Mello P., Milano M. (2022). Do You Like Dancing Robots? AI Can Tell You Why [10.3233/FAIA220064].
Do You Like Dancing Robots? AI Can Tell You Why
De Filippo A.
;Mello P.;Milano M.
2022
Abstract
Humanoid robots have been successfully used in artistic research areas, and many works have studied and implemented systems for robotic dance. However, only few works take into account the human evaluation of these artistic outputs. This work makes a step in the direction of addressing the complex task of defining criteria for the evaluation of robotic dance performances. For this aim, in the context of a Master course on Fundamentals of Artificial Intelligence (AI), we have organized a challenge among our students and the winner is decided on the basis of a questionnaire we defined for robotic dance evaluation. In addition, we created a public dataset that maps the features of each choreography to the judgements provided by audience with different backgrounds on several evaluation targets. Then, we tested various Machine Learning models for predicting the audience evaluation, and we propose a choreography features importance analysis to help both human choreographers and AI algorithms to create dance performances with a major impact on the audience. We also suggest new directions for future interdisciplinary researchFile | Dimensione | Formato | |
---|---|---|---|
FAIA-351-FAIA220064.pdf
accesso aperto
Tipo:
Versione (PDF) editoriale
Licenza:
Licenza per Accesso Aperto. Creative Commons Attribuzione - Non commerciale (CCBYNC)
Dimensione
575.41 kB
Formato
Adobe PDF
|
575.41 kB | Adobe PDF | Visualizza/Apri |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.