PANDORA: Diffusion Policy Learning for Dexterous Robotic Piano Playing
This addresses the challenge of robotic musicianship for applications in entertainment or human-robot interaction, representing an incremental advance by combining existing techniques like diffusion models and LLM feedback for a specific domain.
The paper tackled the problem of enabling dexterous robotic piano playing by developing PANDORA, a diffusion-based policy learning framework that achieved state-of-the-art performance in the ROBOPIANIST environment, significantly outperforming baselines in precision and expressiveness.
We present PANDORA, a novel diffusion-based policy learning framework designed specifically for dexterous robotic piano performance. Our approach employs a conditional U-Net architecture enhanced with FiLM-based global conditioning, which iteratively denoises noisy action sequences into smooth, high-dimensional trajectories. To achieve precise key execution coupled with expressive musical performance, we design a composite reward function that integrates task-specific accuracy, audio fidelity, and high-level semantic feedback from a large language model (LLM) oracle. The LLM oracle assesses musical expressiveness and stylistic nuances, enabling dynamic, hand-specific reward adjustments. Further augmented by a residual inverse-kinematics refinement policy, PANDORA achieves state-of-the-art performance in the ROBOPIANIST environment, significantly outperforming baselines in both precision and expressiveness. Ablation studies validate the critical contributions of diffusion-based denoising and LLM-driven semantic feedback in enhancing robotic musicianship. Videos available at: https://taco-group.github.io/PANDORA