Laban Movement Analysis and Affective Movement Generation for Robots and Other Near-Living Creatures

This manuscript describes an approach, based on Laban Movement Analysis, to generate compact and informative representations of movement to facilitate affective movement recognition and generation for robots and other artificial embodiments. We hypothesize that Laban Movement Analysis, which is a comprehensive and systematic approach for describing movement, is an excellent candidate for deriving a low-dimensional representation of movement which facilitates affective motion modeling. First, we review the dimensions of Laban Movement Analysis most relevant for capturing movement expressivity and propose an approach to compute an estimate of the Shape and Effort components of Laban Movement Analysis using data obtained from motion capture. Within a motion capture environment, a professional actor reproduced prescribed motions, imbuing them with different emotions. The proposed approach was compared with a Laban coding by a certified movement analyst (CMA). The results show a strong correlation between results from the automatic Laban quantification and the CMA-generated Laban quantification of the movements. Based on these results, we describe an approach for the automatic generation of affective movements, by S.J. Burton (&) Sheridan College, Oakville, Canada e-mail: [email protected] A.-A. Samadani Institute of Biomaterials and Biomedical Engineering, Bloorview Research Institute, University of Toronto, Toronto, Canada e-mail: [email protected] R. Gorbet Department of Knowledge Integration, University of Waterloo, Waterloo, Canada e-mail: [email protected] D. Kulić Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, Canada e-mail: [email protected] © Springer International Publishing Switzerland 2016 J.-P. Laumond and N. Abe (eds.), Dance Notations and Robot Motion, Springer Tracts in Advanced Robotics 111, DOI 10.1007/978-3-319-25739-6_2 25 adapting pre-defined motion paths to overlay affective content. The proposed framework is validated through cross-validation and perceptual user studies. The proposed approach has great potential for application in fields including robotics, interactive art, animation and dance/acting training.