We present iGibson 1.0, a novel simulation environment to develop robotic solutions for interactive tasks in large-scale realistic scenes. Our environment contains 15 fully interactive home-sized scenes with 108 rooms populated with rigid and articulated objects. The scenes are replicas of real-world homes, with distribution and the layout of objects aligned to those of the real world. iGibson 1.0 integrates several key features to facilitate the study of interactive tasks: i) generation of high-quality virtual sensor signals (RGB, depth, segmentation, LiDAR, flow and so on), ii) domain randomization to change the materials of the objects (both visual and physical) and/or their shapes, iii) integrated sampling-based motion planners to generate collision-free trajectories for robot bases and arms, and iv) intuitive human-iGibson interface that enables efficient collection of human demonstrations. Through experiments, we show that the full interactivity of the scenes enables agents to learn useful visual representations that accelerate the training of downstream manipulation tasks. We also show that iGibson 1.0 features enable the generalization of navigation agents, and that the human-iGibson interface and integrated motion planners facilitate efficient imitation learning of human demonstrated (mobile) manipulation behaviors. iGibson 1.0 is open-source, equipped with comprehensive examples and documentation. For more information, visit our project website: http://svl.stanford.edu/igibson/
翻译:我们展示了iGibson 1.0,这是为大型现实场景中的互动任务开发机器人解决方案的一种新型模拟环境。我们的环境包含15个完全互动的家庭式场景,有108个房间,有108个房间居住,有僵硬和清晰的物体;现场是真实世界之家的复制品,分布和布局与现实世界的物体一致。iGibson 1.0结合了几个关键特征,以便利对交互式任务的研究:一)生成高质量的虚拟感应信号(RGB、深度、分解、LIDAR、流程等),二)域随机化以改变物体(视觉和物理)和/或其形状的材料,三)基于取样的综合运动规划者为机器人基地和武器制作免碰撞的轨迹,并进行与现实世界相匹配的分布。我们通过实验显示,场面的充分互动使代理者能够学习有用的视觉表现,加快了下游操作任务的培训。我们还表明,iGib 1.0son的功能使导航代理者能够以开放的方式学习G-rodroad-roductions 网站的普通化和展示。