Learning to Move with Style: Few-Shot Cross-Modal Style Transfer for Creative Robot Motion Generation

As robots increasingly participate in creative and social contexts, the ability to generate creative, stylised movements becomes crucial for applications ranging from performance art to human-robot collaboration. We present a novel framework for cross-modal style transfer that enables robots to learn new movement styles by adapting existing human-robot dance collaborations using human movement videos. Our dual-stream architecture processes raw video frames and pose sequences through cross-modal attention mechanisms, capturing rhythm, acceleration patterns, and spatial coordination characteristics of different movement styles. The transformer-based style transfer network generates motion transformations through residual learning while preserving the trajectory of original dance movements, enabling few-shot adaptation using only 3-6 demonstration videos. We evaluate across ballet, jazz, flamenco, contemporary dance and martial arts, introducing a creativity parameter that provides control over the style-trajectory trade-off. Results demonstrate successful style differentiation with overall style transfer scores increasing 6.7x to 7.4x from minimum to maximum creativity settings, advancing human-robot creative collaboration by expanding robots' expressive vocabulary beyond their original choreographic context.