We present AdaFold, a model-based feedback-loop framework for optimizing folding trajectories. AdaFold extracts a particle-based representation of cloth from RGB-D images and feeds back the representation to a model predictive control to re-plan folding trajectory at every time-step. A key component of AdaFold that enables feedback-loop manipulation is the use of semantic descriptors extracted from geometric features. These descriptors enhance the particle representation of the cloth to distinguish between ambiguous point clouds of differently folded cloths. Our experiments demonstrate AdaFold's ability to adapt folding trajectories to cloths with varying physical properties and generalize from simulated training to real-world execution.

翻译：本文提出AdaFold，一种基于模型的反馈循环框架，用于优化布料折叠轨迹。AdaFold从RGB-D图像中提取基于粒子表示的布料表征，并将该表征反馈至模型预测控制器，从而在每一时间步重新规划折叠轨迹。AdaFold实现反馈循环操作的关键组件是利用几何特征提取的语义描述符。这些描述符增强了布料的粒子表征能力，能够区分不同折叠状态下产生的模糊点云。实验表明，AdaFold能够针对具有不同物理属性的布料自适应调整折叠轨迹，并能将从仿真训练获得的模型泛化至真实世界执行任务。