ShapeGen: 机器人数据生成用于类别级操作
ShapeGen: Robotic Data Generation for Category-Level Manipulation
AI总结：
本文提出ShapeGen，一种无需模拟器的3D数据生成方法，通过形状库构建和功能感知生成提升机器人在类别级操作中的几何多样性适应能力。
摘要：
在不受控的现实世界场景中，操作策略面临日常物体在类内几何多样性带来的挑战。为在此类变化下保持鲁棒性，策略需以类别级方式工作，即能与某一类别的任一物体交互，而非仅特定训练物体。此类内泛化通常通过形状多样化训练数据培养；然而，由于需要大量人工劳动和异构物体集合，手动收集此类数据不可行。本文提出ShapeGen，一种无需模拟器的3D数据生成方法，将过程分为两个阶段：形状库构建和功能感知生成。在第一阶段，通过训练形状之间的空间形变映射点到点，将3D模型与形变映射聚合为可插拔的形状库。第二阶段设计了利用现有库仅需最小人工标注即可生成物理合理且功能正确的新演示的管道。现实世界实验验证了ShapeGen提升策略类别级形状泛化能力的有效性。项目页面：https://wangyr22.github.io/ShapeGen/.
英文摘要：
Manipulation policies deployed in uncontrolled real-world scenarios are faced with great in-category geometric diversity of everyday objects. In order to function robustly under such variations, policies need to work in a category-level manner, i.e. knowing how to interact with any object in a certain category, instead of only a specific one seen during training. This in-category generalizability is usually nurtured with shape-diversified training data; however, manually collecting such a corpus of data is infeasible due to the requirement of intense human labor and large collections of divergent objects at hand. In this paper, we propose ShapeGen, a data generation method that aims at generating shape-variated manipulation data in a simulator-free and 3D manner. ShapeGen decomposes the process into two stages: Shape Library curation and Function-Aware Generation. In the first stage, we train spatial warpings between shapes mapping points to points that correspond functionally, and aggregate 3D models along with the warpings into a plug-and-play Shape Library. In the second stage, we design a pipeline that, leveraging established Libraries, requires only minimal human annotation to generate physically plausible and functionally correct novel demonstrations. Experiments in the real world demonstrate the effectiveness of ShapeGen to boost policies' in-category shape generalizability. Project page: https://wangyr22.github.io/ShapeGen/.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
