用真人反馈训练机器人患者，让疼痛声音随按压力度真实变化。

Diagnostic errors remain a major cause of preventable mortality, particularly in resource limited settings. Medical training simulators, including robopatients, help reduce such errors by replicating patient responses during procedures such as abdominal palpation. However, generating realistic multimodal feedback especially auditory pain expressions remains challenging due to the complex, nonlinear relationship between applied palpation forces and perceived pain sounds. The high dimensionality and perceptual variability of pain vocalizations further limit conventional modeling approaches. We propose a novel experimental paradigm for adaptive pain expressivity in robopatients that dynamically generates auditory pain responses to palpation forces using human in the loop machine learning. Specifically, we employ Proximal Policy Optimization (PPO), a reinforcement learning algorithm suited for continuous control, to iteratively refine pain sound generation based on real time human evaluative feedback. The system initializes randomized mappings between force inputs and sound outputs, and the learning agent progressively adjusts them to align with human perceptual preferences. Results show that the framework adapts to individual palpation behaviors and subjective sound preferences while capturing a broad range of perceived pain intensities, from mild discomfort to acute distress. We also observe perceptual saturation at lower force ranges, with gender specific thresholds in pain sound perception. This work demonstrates the feasibility of human in the loop reinforcement learning for co-optimizing haptic input and auditory pain expression in medical simulators, highlighting the potential of adaptive and immersive platforms to enhance palpation training and reduce diagnostic errors.