iGibson 1.0: a Simulation Environment for Interactive Tasks in Large Realistic Scenes (2012.02924v6)

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 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/

• The paper introduces iGibson 1.0, a simulation environment replicating 15 detailed home scenes to support interactive robotic tasks.
• The paper integrates advanced sensor outputs—including RGB, depth, LiDAR, and segmentation—to enable robust visual representation learning.
• The paper leverages domain randomization and sampling-based motion planners to enhance sim-to-real transfer and collision-free trajectory planning.

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

This essay explores the features, capabilities, and implications of the iGibson 1.0, a comprehensive simulation environment developed to advance robotic solutions for interactive tasks in expansive, realistic environments. iGibson 1.0 encompasses an innovative architectural design, allowing for the fully interactive simulation of robotic manipulation and navigation in replicas of real-world environments.

Key Features

Scene Realism and Interactivity: iGibson 1.0 presents 15 highly detailed home-sized scenes containing 108 rooms. These scenes are meticulous replicas of actual homes, maintaining authentic object distribution and layout to enable realistic robotic task simulation. The simulation adheres to precise interaction dynamics between robots and a multiplicity of articulated and rigid objects, posited through an integrated physics engine.

Advanced Sensor Integration: The platform provides high-quality virtual sensor outputs, including RGB, depth, segmentation, LiDAR, optical and scene flow. These sensor modalities are crucial for visual representation learning, enabling robots to perceive and interact effectively with complex environments.

Domain Randomization: iGibson incorporates domain randomization functionalities, allowing variations in material properties, object shapes, and appearances to enhance the robustness and generalization capabilities of perceptual and control policies. This aspect is vital for sim-to-real transfer, bridging the gap between virtual simulation and real-world application.

Motion Planning: The platform is equipped with embedded sampling-based motion planners, such as RRT and BiRRT, essential for devising collision-free trajectories for both navigation and manipulation tasks, thereby expanding the operational efficiency of simulated agents.

Human Interaction Interface: With an intuitive human-iGibson interface, researchers can collect human demonstrations efficiently, facilitating imitation learning for complex manipulation and mobile manipulation tasks. This feature is quintessential for exploring human-robot interaction dynamics and optimization of task execution strategies.

Experimental Insights

Research conducted using iGibson 1.0 highlights its efficacy in training robust visuomotor policies. Navigation and Generalization: Robots trained within this environment demonstrated enhanced generalization capabilities across novel scenes, facilitated by the sophisticated sensor simulations and domain randomizations. Imitation Learning: The human-interaction interface enables rapid collection of effective demonstrations, underpinning the development of imitation learning algorithms for manipulation tasks with high success rates. Visual Representation Learning: The fully interactive environment fosters the learning of intermediary visual representations, significantly accelerating the training of downstream manipulation objectives.

Implications and Future Directions

From a practical standpoint, iGibson's facility for comprehensive scene interaction and extensive sensor simulation positions it as a pivotal tool for the development and testing of robotic solutions prior to real-world deployment. Theoretically, it offers substantial contributions to the understanding and modeling of context-driven, perception-guided robotic actions.

Future research could investigate the deeper integration of AI-based methods to intuitively manage the complexity of interactions in dense, dynamic environments, potentially elevating capabilities in adaptive learning and autonomous decision-making. Moreover, expanding the dataset with additional scenes and varying environmental contexts could further improve generalization and resilience, enhancing applicability in diversified real-world scenarios.

In conclusion, iGibson 1.0 represents a substantial step forward in simulation environments, enabling significant advancements in embodied AI and robotics through its unique combination of realism, interactivity, and comprehensive toolsets. It provides the research community with the capacity to simulate and test sophisticated robotic behaviors in conditions mirroring real-world challenges, laying the groundwork for revolutionary developments in interactive task automation.