iGibson 1.0: A Simulation Environment for Interactive Tasks in Large Realistic Scenes

2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)(2021)

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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-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/.
interactive tasks,simulation environment,robotic solutions,large-scale realistic scenes,fully interactive home-sized scenes,rigid objects,articulated objects,real-world homes,high-quality virtual sensor signals,sampling-based motion planners,robot bases,human-iGibson interface,iGibson 1.0,object distribution,object layout,collision-free trajectories,robot arms,human demonstrations,visual representations,navigation agents,imitation learning,manipulation behaviors
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