Papers › READ: Retrieval-Enhanced Asymmetric Diffusion for Motion Planning
READ: Retrieval-Enhanced Asymmetric Diffusion for Motion Planning
Takeru Oba, Matthew Walter, Norimichi Ukita
This paper proposes Retrieval-Enhanced Asymmetric Diffusion (READ) for image-based robot motion planning. Given an image of the scene READ retrieves an initial motion from a database of image-motion pairs and uses a diffusion model to refine the motion for the given scene. Unlike prior retrieval-based diffusion models that require long forward-reverse diffusion paths READ directly diffuses between the source (retrieved) and target motions resulting in an efficient diffusion path. A second contribution of READ is its use of asymmetric diffusion whereby it preserves the kinematic feasibility of the generated motion by forward diffusion in a low-dimensional latent space while achieving high-resolution motion by reverse diffusion in the original task space using cold diffusion. Experimental results on various manipulation tasks demonstrate that READ outperforms state-of-the-art planning methods while ablation studies elucidate the contributions of asymmetric diffusion.
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