iGibson 1.0: A Simulation Environment for Interactive Tasks in Large Realistic Scenes
Bokui Shen∗, Fei Xia∗, Chengshu Li∗, Roberto Martín-Martín∗, Linxi Fan, Guanzhi Wang, Claudia Pérez-D’Arpino, Shyamal Buch, Sanjana Srivastava, Lyne Tchapmi, Micael Tchapmi, Kent Vainio, Josiah Wong, Li Fei-Fei, Silvio Savarese ∗Equal contribution. All authors are with the Stanford Vision、Learning Laboratory, Stanford University.
Abstract
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 features enable the generalization of navigation agents, and that the human-i
中文速览
机器人研究长期面临一个痛点：现有仿真环境要么场景太小太简单，要么场景够大却不支持真实的物理交互，导致在仿真里学到的技能很难迁移到真实机器人上。iGibson 1.0 正是为填补这一空白而生，它提供了 15 个按真实家居 1:1 还原的大型可交互场景（共 108 个房间），场景里的刚体和铰接物体均受物理引擎驱动，机器人可以真正"动"它们。系统还集成了高质量传感器模拟（RGB、深度、LiDAR、光流等）、材质与形状的域随机化、无碰撞运动规划器，以及方便采集人类示范的图形界面。实验证明，丰富的场景交互性能帮助智能体学到更有用的视觉表征，加速下游操作任务的训练，同时域随机化显著提升了导航策略的泛化能力，人机界面与运动规划器也让模仿学习的数据采集效率大幅提高。这项工作以开源形式发布，为需要在逼真家居环境中研究导航、操作乃至移动操作的机器人学界提供了一个统一、易用的基准平台。
原文 arXiv:2012.02924;中英对照 + 大白话阅读 https://aha.fim.ai/paper/2012.02924v6