DRUM: Real-Time Dancing Robots Using Music and Execution Feedback

Interactive robot dancing requires a humanoid to respond to musical input throughout a performance while maintaining coherent and stable motion. Two representative deployment pipelines fall short of this goal: choreography-specific policies require additional training for new routines, whereas offline music-to-motion generators can produce references poorly matched to a general tracker. When either pipeline executes a fixed sequence, upcoming movements cannot be regenerated to accommodate changing music or deviations in the robot's actual state. We present DRUM, a framework for real-time humanoid dancing that combines streaming music-conditioned generation with execution-feedback training. Our streaming model generates successive dance segments, allowing updated music and motion history to shape upcoming movements. To make these movements compatible with an existing tracker, we post-train the generator while keeping the tracker frozen. This training alternates reward-based adaptation of the generated motion distribution with learning conditioned on the tracker's executed motion histories. The two feedback signals address complementary needs: learning what the tracker can execute and how to continue from the states it actually reaches. Together, they connect musical responsiveness with execution-aware motion continuation, reducing the mismatch between planned and realized dance. Experiments demonstrate state-of-the-art performance in streaming dance generation and execution robustness. Real-world humanoid demonstrations further showcase continuous dancing and responsive transitions under user-driven music changes.