Movement Matters

摘要

This paper explores movement and its capacity for meaning-making and eliciting affect in human-robot interaction. Bringing together creative robotics, dance and machine learning, our research project develops a novel relational approach that harnesses dancers' movement expertise to design a non-anthropomorphic robot, its potential to move and capacity to learn. The project challenges the common assumption that robots need to appear human or animal-like to enable people to form connections with them. Our performative body-mapping (PBM) approach, in contrast, embraces the difference of machinic embodiment and places movement and its connection-making, knowledge-generating potential at the center of our social encounters. The paper discusses the first stage of the project, in which we collaborated with dancers to study how movement propels the becoming-body of a robot, and outlines our embodied approach to machine learning, grounded in the robot's performative capacity.