PANDORA: Diffusion Policy Learning for Dexterous Robotic Piano Playing

The pipeline begins with the robot state \(s_t\) and goal state \(g_t\) that condition a U-Net (via FiLM) to iteratively denoise an initial noisy action \(x_t\) into \(x_0\) using DDIM. The denoised action \(x_0\) is then added to the IK solver’s output \(q\) (residual combination) to form the final action, which is executed in MuJoCo. An Oracle Reward module, driven by a large language model, evaluates the performance based on style and accuracy.

Abstract

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.