用统一表示学习软体物体形变，提升机器人抓取泛化能力
Shape-Space Deformer: Unified Visuo-Tactile Representations for Robotic Manipulation of Deformable Objects
- 通过模板增强构建统一形变表示，实现精细重建
- 在未见受力下仍保持高精度，优于现有方法
- 适合实时应用，可快速适应新物体
准确建模物体形变对一系列机器人操作任务至关重要，尤其在处理软体或可变形物体时。当前方法难以泛化到未见的外力或适应新物体，限制了其在真实场景中的应用。我们提出Shape-Space Deformer，一种基于模板增强的统一表征方法，可编码多样化的物体形变，实现鲁棒且细粒度的重建，对异常值和伪影具有强抵抗力。该方法显著提升了对未见外力的泛化能力，并能快速适应新物体。我们在多种力泛化设置下进行大量实验，评估其重建未见形变的能力，结果表明在重建精度和鲁棒性上均有显著提升。该方法支持实时性能，适用于下游操纵任务。
原文摘要 · Abstract (English)
Accurate modelling of object deformations is crucial for a wide range of robotic manipulation tasks, where interacting with soft or deformable objects is essential. Current methods struggle to generalise to unseen forces or adapt to new objects, limiting their utility in real-world applications. We propose Shape-Space Deformer, a unified representation for encoding a diverse range of object deformations using template augmentation to achieve robust, fine-grained reconstructions that are resilient to outliers and unwanted artefacts. Our method improves generalization to unseen forces and can rapidly adapt to novel objects, significantly outperforming existing approaches. We perform extensive experiments to test a range of force generalisation settings and evaluate our method's ability to reconstruct unseen deformations, demonstrating significant improvements in reconstruction accuracy and robustness. Our approach is suitable for real-time performance, making it ready for downstream manipulation applications.
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