SoftMimicGen：一种用于可变形物体操作规模化机器人学习的数据生成系统
SoftMimicGen: A Data Generation System for Scalable Robot Learning in Deformable Object Manipulation
AI总结：
本文提出SoftMimicGen，一种自动化数据生成流水线，用于可变形物体操作任务，涵盖多种真实物体和行为，通过高保真模拟环境生成数据并训练高性能策略。
摘要：
大规模机器人数据集已促进了广泛机器人操作技能的学习，但这些数据集仍难以收集和扩展，因为需要大量的人力、时间和成本。仿真和合成数据生成已被证明是满足数据需求的有效替代方案，特别是近期研究表明此类合成数据集可以大幅减少现实世界数据需求并促进对新场景的泛化。然而，这种范式局限于刚体任务，而可变形物体操作涵盖了大量现实世界操作，仍是需要解决的关键缺口。本文介绍了SoftMimicGen，一种自动化数据生成流水线，用于可变形物体操作任务。我们引入了一套高保真模拟环境，涵盖多种可变形物体（填充动物、绳子、组织、毛巾）和操作行为（高精度穿线、动态鞭打、折叠、拾取放置），涵盖四种机器人形态：单臂机械臂、双臂机械臂、人形机器人和手术机器人。我们应用SoftMimicGen生成任务套件的数据集，从数据中训练高性能策略，并系统分析数据生成系统。项目网站：https://softmimicgen.github.io
英文摘要：
Large-scale robot datasets have facilitated the learning of a wide range of robot manipulation skills, but these datasets remain difficult to collect and scale further, owing to the intractable amount of human time, effort, and cost required. Simulation and synthetic data generation have proven to be an effective alternative to fuel this need for data, especially with the advent of recent work showing that such synthetic datasets can dramatically reduce real-world data requirements and facilitate generalization to novel scenarios unseen in real-world demonstrations. However, this paradigm has been limited to rigid-body tasks, which are easy to simulate. Deformable object manipulation encompasses a large portion of real-world manipulation and remains a crucial gap to address towards increasing adoption of the synthetic simulation data paradigm. In this paper, we introduce SoftMimicGen, an automated data generation pipeline for deformable object manipulation tasks. We introduce a suite of high-fidelity simulation environments that encompasses a wide range of deformable objects (stuffed animal, rope, tissue, towel) and manipulation behaviors (high-precision threading, dynamic whipping, folding, pick-and-place), across four robot embodiments: a single-arm manipulator, bimanual arms, a humanoid, and a surgical robot. We apply SoftMimicGen to generate datasets across the task suite, train high-performing policies from the data, and systematically analyze the data generation system. Project website: \href{https://softmimicgen.github.io}{softmimicgen.github.io}.
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。
