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 tasks and a real world robotic setup on both state and image observations. MPD outperforms state-of-the-art DIL methods in success rate, motion quality, and data efficiency.

翻译：机器人辅助手术中的策略学习缺乏数据高效且通用的方法，难以在精细手术干预中展现出理想的运动质量。为此，我们提出运动基元扩散（MPD）——一种用于机器人辅助手术模仿学习的新型方法，专注于对可变形物体的轻柔操作。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。这种结合使得MPD能够实现对可变形物体的轻柔操作，同时保持数据高效性——这对于示范数据稀缺的机器人辅助手术应用至关重要。我们在多种模拟任务和真实机器人平台上，基于状态观测和图像观测对MPD进行了评估。结果表明，MPD在成功率、运动质量和数据效率方面均优于当前最先进的基于扩散的模仿学习方法。