用状态空间模型实现复杂衣物的拾取摆放自动平整，性能媲美顶尖方法。
LaGarNet: Goal-Conditioned Recurrent State-Space Models for Pick-and-Place Garment Flattening
- 基于目标条件的递归状态空间模型，学习衣物操作的潜在动态。
- 单策略模型在4种衣物上实现在仿真与真实世界中的成功平整。
- 减少先验假设，利用随机策略与少量人类示范训练，通用性强。
我们提出一种新型的目标条件递归状态空间模型（GC-RSSM），可学习拾取摆放衣物操作的潜在动态。所提方法LaGarNet在性能上达到基于网格方法的最先进水平，首次成功将状态空间模型应用于复杂衣物处理任务。该模型通过覆盖率-对齐奖励，在由随机策略与少量人类示范训练的扩散策略支持的通用流程中收集的数据集上进行训练，显著降低了以往类似方法引入的归纳偏置。我们证明，单一策略的LaGarNet在仿真和真实世界中均能完成四种不同衣物的平整操作。
原文摘要 · Abstract (English)
We present a novel goal-conditioned recurrent state space (GC-RSSM) model capable of learning latent dynamics of pick-and-place garment manipulation. Our proposed method LaGarNet matches the state-of-the-art performance of mesh-based methods, marking the first successful application of state-space models on complex garments. LaGarNet trains on a coverage-alignment reward and a dataset collected through a general procedure supported by a random policy and a diffusion policy learned from few human demonstrations; it substantially reduces the inductive biases introduced in the previous similar methods. We demonstrate that a single-policy LaGarNet achieves flattening on four different types of garments in both real-world and simulation settings.
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