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

By Bokui (William) Shen, Fei Xia, Chengshu Li, Roberto Mart'in-Mart'in, Linxi (Jim) Fan, Guanzhi Wang, Shyamal Buch, C. D'Arpino, Siddharth Srivastava, Lyne P. Tchapmi and 4 more. 2020

246 citations recorded by the source corpus.

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/.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition, Control and Systems Engineering, Robotics & Control

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

In Computer Vision and Pattern Recognition

In Control and Systems Engineering

In Robotics & Control

Citations in the corpus

Habitat 2.0: Training Home Assistants to Rearrange their Habitat (2021, background): “How are interactive simulators built today? Either by relying on game engines [7, 51, 52] or via a ‘homebrew’ integration of existing rendering and physics libraries [8, 35, 37, 53].”
Voyager: An Open-Ended Embodied Agent with Large Language Models (2023, background): “[5] Bokui Shen, Fei Xia, Chengshu Li, Roberto Martín-Martín, Linxi Fan, Guanzhi Wang, Claudia Pérez-D’Arpino, Shyamal Buch, Sanjana Srivastava, Lyne P.”
CORA: Benchmarks, Baselines, and Metrics as a Platform for Continual Reinforcement Learning Agents (2021, unknown): “, 2021), iGibson (Shen et al., 2020), Sapien (Xiang et al.”
AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation (2023, unknown): “…many applications require the presence of agents capable of interacting with environments and making decisions in an online context, such as in game playing (Mnih et al., 2013; Vinyals et al., 2017), web interactions (Liu et al., 2018; Shi et al., 2017), and robot manipulations (Shen et al., 2021).”
Aligning Cyber Space With Physical World: A Comprehensive Survey on Embodied AI (2024, unknown): “Restrictions apply.”
A Survey on Vision--Language--Action Models for Embodied AI (2024, unknown): “Gibson [190] 572/- - - Pybullet Navi RGB, D, N, S - - Navi only - iGibson [189], [191], [192] 15/108 Object States, Task Planning.”
VIMA: General Robot Manipulation with Multimodal Prompts (2022, background): “For example, iGibson (Shen et al., 2020; Li et al., 2021; Srivastava et al., 2021) simulates interactive household scenarios.”
PointOdyssey: A Large-Scale Synthetic Dataset for Long-Term Point Tracking (2023, methodology): “We add diveristy by randomizing our synthetic scenes, in steps similar to iGibson [48].”