让机器人学会柔性物体间的相对放置，支持真实世界复杂变形场景。
Non-rigid Relative Placement through 3D Dense Diffusion
- 提出跨位移机制，用密集扩散模型建模柔性物体间几何关系。
- 在仿真与真实世界中实现未见物体、异常场景和多目标的泛化能力。
- 适合研究机器人抓取与柔性操作的学者，突破传统刚体限制。
相对放置任务旨在预测一个物体相对于另一个物体的摆放位置，例如将杯子放在杯架上。近期基于显式对象中心几何推理的方法在数据高效学习与泛化至未见任务变化方面取得显著进展。然而，这些方法尚未能表征可变形变换，而现实中非刚性物体普遍存在。为此，我们首次提出“跨位移”概念，将相对放置原理扩展至可变形物体间的几何关系，并提出一种基于视觉的密集扩散学习方法。实验表明，该方法可在多个高度可变形任务中泛化至未见物体实例、分布外场景配置及多模态目标，涵盖仿真与真实世界环境，超越现有工作范围。补充信息与视频详见 https://sites.google.com/view/tax3d-corl-2024。
原文摘要 · Abstract (English)
The task of "relative placement" is to predict the placement of one object in relation to another, e.g. placing a mug onto a mug rack. Through explicit object-centric geometric reasoning, recent methods for relative placement have made tremendous progress towards data-efficient learning for robot manipulation while generalizing to unseen task variations. However, they have yet to represent deformable transformations, despite the ubiquity of non-rigid bodies in real world settings. As a first step towards bridging this gap, we propose ``cross-displacement" - an extension of the principles of relative placement to geometric relationships between deformable objects - and present a novel vision-based method to learn cross-displacement through dense diffusion. To this end, we demonstrate our method's ability to generalize to unseen object instances, out-of-distribution scene configurations, and multimodal goals on multiple highly deformable tasks (both in simulation and in the real world) beyond the scope of prior works. Supplementary information and videos can be found at https://sites.google.com/view/tax3d-corl-2024 .
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。