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.

本文研究了为什么像 PlaNet 这样的循环状态空间模型在布料操纵任务中失败，通过在拾放布料平整领域中应用 PlaNet，探讨了模糊的重建如何导致在潜在空间中直接进行规划变得困难，并通过采用 KL 平衡和潜在超调来调整训练损失和计划拾取位置，并将更新后的 PlaNet-Pick 模型应用于潜在 MPC 算法，在模拟中实现了最先进的性能。