RoboCousin：构建您自己的鲁棒双臂机器人操作仿真游乐场
RoboCousin: Build Your Own Simulation Playground for Robust Bimanual Robotic Manipulation
- E-surfing Digital Life Technology Co., Ltd., China Telecom（中国电信翼数字生活科技有限公司）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
RoboCousin是一个基于仿真的数据生成平台，将用户观察转化为资产、场景和专家轨迹，生成超过一百万条双臂操作轨迹，提升仿真到现实的迁移能力。
AI中文摘要：
双臂操作策略需要大规模且多样化的训练数据集，然而在物理机器人上收集示范数据既昂贵又难以扩展。仿真可以高效地生成数据，但现有的流水线通常在封闭的资产库和预定义场景中运行：添加一个新观察到的物体或环境仍然需要大量精力来重建几何形状、指定物理和语义属性、标注交互，并将结果集成到可执行任务中。我们提出了RoboCousin，一个可扩展的基于仿真的数据生成平台，它将用户提供的观察转化为可复用的资产、场景和用于双臂操作的专业轨迹。基于RoboTwin 2.0构建，RoboCousin将物体图像转换为具有视觉和碰撞几何、语义和物理元数据以及自动生成的抓取接触候选的仿真就绪资产。它进一步构建数字孪生体，这些孪生体在保持任务相关可供性和空间关系的同时，变化兼容的物体、背景、布局和语言指令。相同的资产系统支持桌面级和房间级场景构建，并具有碰撞感知的基础控制，以实现在固定工作空间之外的交互。我们发布了RoboCousin-OBD，包含超过3000个标注物体实例和50个背景环境，并使用RoboCousin在50个任务上生成了超过一百万条专家轨迹。仿真和真实机器人实验表明，自动生成的交互标注与精心整理的标注相当，生成的资产提供了有效的仿真到现实监督，并且桌面孪生体可以改善在单一重建场景上训练之外的迁移。因此，RoboCousin为扩展合成双臂操作数据的规模和覆盖范围提供了一条实用路径。
英文摘要：
Bimanual manipulation policies require large and diverse training datasets, yet collecting demonstrations on physical robots is expensive and difficult to scale. Simulation can generate data efficiently, but existing pipelines typically operate within closed asset libraries and predefined scenes: adding a newly observed object or environment still requires substantial effort to reconstruct geometry, specify physical and semantic properties, annotate interactions, and integrate the result into executable tasks. We present RoboCousin, an extensible simulation-based data-generation platform that turns user-provided observations into reusable assets, scenes, and expert trajectories for bimanual manipulation. Built on RoboTwin~2.0, RoboCousin converts object images into simulation-ready assets with visual and collision geometry, semantic and physical metadata, and automatically generated grasp-contact candidates. It further constructs digital cousins that vary compatible objects, backgrounds, layouts, and language instructions while preserving task-relevant affordances and spatial relations. The same asset system supports tabletop and room-level scene construction, with collision-aware base control for interaction beyond a fixed workspace. We release RoboCousin-OBD, containing more than 3,000 annotated object instances and 50 background environments, and use RoboCousin to generate over one million expert trajectories across 50 tasks. Simulation and real-robot experiments show that the automatically generated interaction annotations are comparable to curated annotations, generated assets provide effective sim-to-real supervision, and tabletop cousins can improve transfer beyond training on a single reconstructed scene. RoboCousin therefore provides a practical path for expanding both the scale and coverage of synthetic bimanual manipulation data.
