iGibson: Interactive Gibson Benchmark for Real-World Navigation and Manipulation

iGibson is the standard simulation platform for indoor robot navigation and manipulation research. Built on PyBullet for physics and iGibson renderer for photorealistic RGB, the platform provides 15 fully interactive scanned scenes (apartments, offices, houses) with 500+ object categories that can be opened, closed, picked, and placed. Unlike synthetic environments like Habitat or AI2-THOR, iGibson scenes are reconstructed from real-world scans, preserving accurate geometry, lighting, and object arrangements. iGibson 2.0 adds interactive physics: doors have hinges, drawers slide on rails, and liquids pour with particle simulation. This enables training policies for complex long-horizon tasks like "make coffee" (fill kettle → boil water → pour into cup) or "tidy the living room" (pick up objects → navigate to storage → place). The observation space includes RGB, depth, semantic segmentation, and scene graphs, making it compatible with both pure-vision RL and VLA models. The benchmark suite includes 50+ tasks: point-goal navigation, object-goal navigation, social navigation (avoiding virtual humans), rearrangement, and manipulation. Evaluation is standardized with generalization splits: train on apartment A, test on apartment B with different furniture layouts. iGibson is the evaluation platform for BEHAVIOR-1K, the largest embodied AI benchmark to date. For practitioners, iGibson installs via `pip install igibson` and integrates with Gymnasium, ROS2, and Habitat APIs. The asset downloader fetches scenes on-demand (total ~50GB). The included ROS2 bridge lets you run SLAM and navigation stacks (e.g., FAST-LIO2 + Nav2) in simulation before real-world deployment.