Real-World Data for Habitat
Habitat evaluates embodied AI in photorealistic simulation. Real-world data validates whether those policies work in actual buildings.
Habitat at a Glance
Habitat Task Categories
Habitat's tasks span navigation, manipulation, and social interaction, each with distinct sim-to-real challenges.
| Task | Input | Metric | Sim-to-Real Gap |
|---|---|---|---|
| PointNav (navigate to coordinates) | RGB-D + GPS/compass | Success + SPL | Real sensor noise, dynamic obstacles |
| ObjectNav (navigate to object) | RGB-D + object category | Success + SPL | Object recognition in clutter, unseen objects |
| Pick & Place | RGB-D + arm state | Task completion | Contact dynamics, object weight, grasp stability |
| Rearrangement | RGB-D + goal config | Displacement reduction | Functional organization, semantic understanding |
| Social Navigation | RGB-D + human pose | Path efficiency + safety | Unpredictable human behavior, social conventions |
Habitat vs. Related Embodied AI Platforms
| Feature | Habitat | AI2-THOR | iGibson | BEHAVIOR-1K |
|---|---|---|---|---|
| Visual source | Real 3D scans (HM3D) | Artist-created | Real scans + procedural | Procedural + scans |
| Scenes | 1,000+ (HM3D) | 120 rooms | 15 buildings | 50 scenes |
| Human avatars | Yes (v3.0) | No | No | Yes |
| Manipulation | v2.0+ (mobile manip) | Discrete interactions | Continuous control | Full physics |
| Annual challenge | Yes (since 2019) | RoboTHOR challenge | No | No |
Benchmark Profile
Habitat is an embodied AI simulation platform from Meta FAIR that evaluates navigation and rearrangement in photorealistic 3D indoor environments. Habitat 2.0 and 3.0 introduced articulated object interaction and human-in-the-loop evaluation, making it the primary benchmark for embodied AI research.
The Sim-to-Real Gap
Habitat environments are created from real 3D scans (HM3D, MP3D) providing good visual fidelity, but object physics are simplified. Navigation policies trained in Habitat often fail in real buildings due to unmodeled obstacles (cords, rugs), dynamic elements (people, pets), and sensor noise. The gap between simulated and real depth sensors is a persistent challenge.
Real-World Data Needed
Real indoor navigation trajectories with depth and RGB in diverse buildings. Rearrangement demonstrations in real homes showing object manipulation in context. Social navigation data with real humans to train policies that handle dynamic pedestrians.
Complementary Claru Datasets
Egocentric Activity Dataset
Real-world indoor navigation and activity video from 100+ locations provides visual pretraining data with authentic building layouts, lighting, and obstacles.
Custom Indoor Navigation Collection
Purpose-collected navigation trajectories in real buildings with depth sensors provides ground-truth for validating Habitat-trained navigation policies.
Custom Rearrangement Collection
Real-world object rearrangement demonstrations in authentic homes — moving items between rooms, organizing shelves — provides the manipulation-in-context data Habitat evaluates.
Bridging the Gap: Technical Analysis
Habitat is the most widely used platform for embodied AI navigation research. Its photorealistic environments, built from real 3D scans of homes and offices, provide the best available visual fidelity for indoor simulation. However, scan-based environments are static — furniture does not move, doors do not open naturally, and human inhabitants are absent.
Habitat 3.0 introduced human avatars for social navigation, but simulated humans follow scripted or learned behavior patterns that poorly approximate real human unpredictability. A robot navigating a real home encounters people who suddenly change direction, children running, pets underfoot, and objects left in unexpected places. Training social navigation policies requires data from real shared spaces with actual human activity.
The rearrangement task highlights a different gap. In Habitat, rearrangement means moving objects to specified goal positions — but real-world rearrangement involves understanding functional organization (dishes go in the cabinet near the sink, not alphabetically). This semantic understanding requires data that captures how real humans organize their spaces.
Claru's egocentric activity dataset is directly relevant to Habitat's evaluation paradigm. It captures humans navigating through and interacting with real indoor environments — providing the ground-truth visual and behavioral data that Habitat-trained policies need for validation.
Key Papers
Frequently Asked Questions
Real buildings contain unmodeled obstacles (cords, rugs, pets), dynamic elements (people, doors), and sensor noise. Habitat environments from 3D scans are static and clean. Policies learn to exploit this cleanliness and fail when confronted with real-world clutter and unpredictability.
Rearrangement involves moving objects to goal configurations. In Habitat, goals are specified positions. In reality, rearrangement requires understanding functional organization — how humans actually organize spaces. Data from real homes captures these semantic patterns.
Habitat 3.0 introduced human avatars but they follow simplified behavior models. Real human navigation is unpredictable — people stop suddenly, change direction, carry items, and congregate in doorways. Data from real shared spaces trains policies that handle authentic human behavior.
Winning Habitat Challenge entries typically achieve 80-90%+ success on navigation tasks in simulation but drop to 50-70% on real robots. The gap comes from unmodeled obstacles, dynamic elements, sensor noise, and the difference between static scanned environments and living spaces that change daily.
3D scans capture building geometry at one moment. Real buildings are dynamic — furniture moves, doors open and close, clutter accumulates, lighting changes hourly, and people are present. Policies trained on static scans learn to navigate fixed topology rather than adapting to the changing environment of a real home.
Real-World Navigation Data for Embodied AI
Discuss indoor navigation and rearrangement data for validating Habitat-trained policies.