Policy learning in robot-assisted surgery (RAS) lacks data efficient and versatile methods that exhibit the desired motion quality for delicate surgical interventions. To this end, we introduce Movement Primitive Diffusion (MPD), a novel method for imitation learning (IL) in RAS that focuses on gentle manipulation of deformable objects. The approach combines the versatility of diffusion-based imitation learning (DIL) with the high-quality motion generation capabilities of Probabilistic Dynamic Movement Primitives (ProDMPs). This combination enables MPD to achieve gentle manipulation of deformable objects, while maintaining data efficiency critical for RAS applications where demonstration data is scarce. We evaluate MPD across various simulated and real world robotic tasks on both state and image observations. MPD outperforms state-of-the-art DIL methods in success rate, motion quality, and data efficiency. Project page: https://scheiklp.github.io/movement-primitive-diffusion/

翻译：在机器人辅助手术（RAS）中，策略学习缺乏数据高效且通用的方法，难以展现精细手术干预所需的运动质量。为此，我们提出了运动基元扩散（MPD），一种用于RAS中模仿学习（IL）的新方法，专注于柔性物体的轻柔操作。该方法将基于扩散的模仿学习（DIL）的通用性与概率动态运动基元（ProDMPs）的高质量运动生成能力相结合。这种结合使MPD能够实现对柔性物体的轻柔操作，同时保持对演示数据稀缺的RAS应用至关重要的数据效率。我们在多种模拟和真实世界机器人任务中，基于状态和图像观测评估了MPD。MPD在成功率、运动质量和数据效率方面均优于最先进的DIL方法。项目页面：https://scheiklp.github.io/movement-primitive-diffusion/