AutoRSR：用于可扩展机器人学习的自动化现实到仿真到现实的数据生成
AutoRSR: Automated Real-to-Sim-to-Real Data Generation for Scalable Robot Learning
AI总结：
AutoRSR是一种自动化现实到仿真到现实框架，可将真实工作空间转换为仿真资产与操作数据，用其训练的机器人策略性能媲美人类遥操作训练的模型，人力成本大幅降低。
摘要：
具身机器人学习的扩展需要大量且多样化的操作数据集，但收集真实世界演示在人力和机器人时间上成本高昂。仿真提供了一种可扩展的替代方案，然而将真实工作空间转换为机器人学习可用的环境仍需要大量手动工作，包括对象提取、资产处理、坐标校准和轨迹指定。我们提出AutoRSR，一种高度自动化的现实到仿真到现实框架，可将扫描的真实世界工作空间转换为可重用的具身仿真资产和可扩展的操作数据。给定多视图观测、机器人状态和开放词汇语义查询，AutoRSR会重建工作空间，提取单个对象资产，并以公制尺度自动将重建场景注册到机器人基座坐标系。从少量参考运动开始，AutoRSR随后生成可执行的操作轨迹，并通过轨迹插值和域随机化对其进行扩展。在工作空间扫描和语义查询之后，剩余流程无需手动对象标注、场景到机器人校准或机器人遥操作。在三个真实世界操作任务中，每个任务使用1000个自动生成的回合训练的策略，达到73.3%至86.7%的真实世界成功率，可与使用150个人类遥操作演示训练的pi 0.5相媲美，同时将数据生成所需的人力减少了一个数量级。这些结果表明，真实世界观测可以转换为公制对齐、可执行且可扩展的仿真数据，且人力投入大幅减少。
英文摘要：
Scaling embodied robot learning requires large and diverse manipulation datasets, but collecting real-world demonstrations remains expensive in human effort and robot time. Simulation provides a scalable alternative, yet converting a real workspace into a robot-learning-ready environment still requires substantial manual effort for object extraction, asset processing, coordinate calibration, and trajectory specification. We present AutoRSR, a highly automated real-to-sim-to real framework that converts scanned real-world workspaces into reusable embodied simulation assets and scalable manipulation data. Given multi-view observations, robot states, and open-vocabulary semantic queries, AutoRSR reconstructs the workspace, extracts individual object assets, and automatically registers the reconstructed scene to the robot base frame at metric scale. Starting from a small number of reference motions, AutoRSR then generates executable manipulation trajectories and scales them through trajectory interpolation and domain randomization. After workspace scanning and semantic queries, the remaining pipeline requires no manual object annotation, scene-to-robot calibration, or robot teleoperation. Across three real-world manipulation tasks, policies trained on 1K automatically generated episodes per task achieve 73.3--86.7% real-world success rates, comparable to pi 0.5 trained from 150 human teleoperated demonstrations, while reducing the human time required for data generation by an order of magnitude. These results demonstrate that real-world observations can be converted into metrically aligned, executable, and scalable simulation data with substantially reduced human effort.
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。
