PlaNet-Pick: Effective Cloth Flattening Based on Latent Dynamic Planning
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Why do Recurrent State Space Models such as PlaNet fail at cloth manipulation tasks? Recent work has attributed this to the blurry reconstruction of the observation, which makes it difficult to plan directly in the latent space. This paper explores the reasons behind this by applying PlaNet in the pick-and-place cloth-flattening domain. We find that the sharp discontinuity of the transition function on the contour of the article makes it difficult to learn an accurate latent dynamic model. By adopting KL balancing and latent overshooting in the training loss and adjusting the planned picking position to the closest part of the cloth, we show that the updated PlaNet-Pick model can achieve state-of-the-art performance using latent MPC algorithms in simulation.
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