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