Movement Primitive Diffusion: Learning Gentle Robotic Manipulation of Deformable Objects

Abstract

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/

References in corpus (6)

• DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps
• Autonomous Tissue Manipulation via Surgical Robot Using Learning Based Model Predictive Control
• Sim-To-Real Transfer for Visual Reinforcement Learning of Deformable Object Manipulation for Robot-Assisted Surgery
• Sim-to-Real Surgical Robot Learning and Autonomous Planning for Internal Tissue Points Manipulation using Reinforcement Learning
• Cooperative Assistance in Robotic Surgery through Multi-Agent Reinforcement Learning
• LapGym -- An Open Source Framework for Reinforcement Learning in Robot-Assisted Laparoscopic Surgery

Cited by in corpus (3)

• Diverse Controllable Diffusion Policy with Signal Temporal Logic
• SurgiPose: Estimating Surgical Tool Kinematics from Monocular Video for Surgical Robot Learning
• LUDO: Low-Latency Understanding of Deformable Objects using Point Cloud Occupancy Functions