MoDE, a mixture-of-experts diffusion transformer with noise-conditioned routing, reports state-of-the-art results on CALVIN and LIBERO with lower inference FLOPs than dense baselines.
Movement Primitive Diffusion: Learning Gentle Robotic Manipulation of Deformable Objects
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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/
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Efficient Diffusion Transformer Policies with Mixture of Expert Denoisers for Multitask Learning
MoDE, a mixture-of-experts diffusion transformer with noise-conditioned routing, reports state-of-the-art results on CALVIN and LIBERO with lower inference FLOPs than dense baselines.