Habitat Alternative: Real-World Data for Production Robot Deployment

Habitat is the most important simulation platform for embodied AI research, and its 10,000+ FPS rendering speed enables training that would be impractical in the real world. However, simulation-trained policies face a fundamental challenge when deployed on physical robots: the sim-to-real gap. Rendered images lack real sensor noise, simulated physics approximate but do not replicate real contact dynamics, and scanned residential scenes do not match production deployment environments. Claru provides the real-world data that bridges this gap. Our demonstrations are collected on your physical robot, in your deployment environment, with real sensors capturing the noise, contact dynamics, and visual complexity that your policy must handle in production. For teams using a sim-then-real pipeline, Claru data serves as the final fine-tuning stage that converts simulation-pretrained policies into deployable systems. For teams working directly with real-world data, Claru eliminates the sim-to-real problem entirely. All data ships under a commercial license in standard robotics formats.

Habitat is a high-performance 3D simulation platform for embodied AI research, developed by Meta AI (formerly Facebook AI Research). The original Habitat platform was published by Manolis Savva, Abhishek Kadian, Oleksandr Maksymets, and colleagues in 2019 (arXiv 1904.01201), establishing a flexible simulator that could render photorealistic scenes from Matterport3D environments at several thousand frames per second on a single GPU -- and over 10,000 FPS multi-process. This speed advantage -- roughly 850x faster than real-time -- made Habitat the platform of choice for training navigation and interaction policies that require billions of environment steps.

Habitat 2.0 (Szot et al., arXiv 2106.14405, NeurIPS 2021) extended the platform with physics-enabled interactive environments. It introduced ReplicaCAD, an artist-authored dataset of reconfigurable apartments with articulated objects -- cabinets and drawers that open and close, appliances that can be manipulated, and objects that respond to physical interaction. Habitat 2.0 achieved over 25,000 simulation steps per second on an 8-GPU node and introduced the Home Assistant Benchmark (HAB) for mobile manipulation and object rearrangement, where a simulated Fetch robot must pick, place, and rearrange objects across receptacles while opening and closing containers.

Habitat 3.0 (Puig et al., arXiv 2310.13724, 2023) added human avatar simulation, enabling research on human-robot collaboration in shared spaces. The platform now supports training agents that must coordinate with simulated humans performing household activities, covering tasks like social navigation (moving around people) and social rearrangement (tidying a room while a human is present). The underlying scene dataset, Habitat-Matterport 3D (HM3D, arXiv 2109.08238), contains 1,000 building-scale 3D reconstructions covering 112,500 square meters of navigable space -- 1.4 to 3.7x larger than other indoor 3D datasets like Matterport3D and Gibson.

Habitat has become the dominant platform for embodied AI challenges, hosting annual competitions at NeurIPS and CVPR for tasks including point navigation, object navigation, rearrangement, and social navigation. The simulator itself is released under an MIT license, though the scene datasets (HM3D, ReplicaCAD) carry CC BY-NC restrictions. Habitat's influence on embodied AI research is substantial, but its role as a simulation platform -- rather than a real-world data source -- creates a fundamental distinction that matters for production robot deployment.

The sim-to-real gap is Habitat's fundamental production challenge. Despite photorealistic rendering, simulated environments differ from the real world in ways that compound during deployment: lighting has no dynamic shadows from moving objects, surfaces lack the micro-texture variations that affect real grasping, rendered depth has none of the noise patterns of real depth sensors (structured light artifacts, stereo matching failures, reflective surface dropouts), and simulated physics engines use approximate contact models that do not capture the subtleties of real-world friction, deformation, and slip. Policies trained exclusively in Habitat often achieve high success rates in simulation but fail to transfer reliably to physical robots without extensive sim-to-real adaptation -- a problem that the embodied AI community has studied extensively but not fully solved.

The HM3D scene dataset carries a CC BY-NC license that restricts commercial use. While the Habitat simulator itself is MIT-licensed, training on HM3D scenes for commercial deployment may violate the scene dataset's license terms. Teams building commercial products must carefully audit which scene datasets they train on, and the most photorealistic scenes available for Habitat are generally non-commercial. This creates a practical barrier for production teams that need legally clear training data.

Habitat's manipulation capabilities are limited compared to the demands of production manipulation. Habitat 2.0 introduced interactive objects and a simulated Fetch robot, but the grasping model uses simplified contact mechanics -- objects are often attached to the gripper with constraint-based methods rather than true frictional contact. There is no support for soft or deformable objects, and the range of manipulation primitives is restricted to navigation, pick-place, and container interaction. Dexterous manipulation, tool use, bimanual coordination, and force-sensitive tasks -- common requirements in production -- are not well-supported by the current simulation.

Scene diversity, while large in absolute terms, is constrained to scanned indoor residential environments. HM3D contains 1,000 building-scale reconstructions, but these are primarily houses and apartments. Warehouses, factories, retail stores, hospital rooms, commercial kitchens, and outdoor environments -- common production deployment sites -- are underrepresented or absent. The visual distribution of Habitat training data may not match the visual distribution of your deployment environment, requiring additional real-world data to bridge the appearance gap.

Habitat excels as a rapid prototyping and pretraining environment for navigation policies. Its 10,000+ FPS simulation speed enables training that would take months in the real world to complete in hours. For tasks like point-goal navigation, object-goal navigation, and high-level rearrangement planning, Habitat provides the iteration speed needed for research and initial policy development. If your product includes a navigation component, prototyping in Habitat before real-world deployment is a sound engineering practice.

Habitat is also the standard platform for embodied AI benchmarks. The annual Habitat Challenge at NeurIPS and CVPR defines the state of the art for indoor navigation and rearrangement tasks. If your research involves these tasks or you need to compare your methods against published baselines, Habitat is the necessary evaluation platform.

Switch to real-world data when you need reliable manipulation in production. Simulation cannot faithfully capture the contact dynamics, sensor noise, and visual complexity that determine manipulation success on physical robots. Domain randomization can partially bridge the sim-to-real gap for some tasks, but contact-rich manipulation -- grasping, assembly, tool use, force-controlled insertion -- consistently requires real-world demonstration data to achieve production success rates. Claru provides this data with the full sensor stack from your actual deployment conditions.

The most effective approach for teams doing both navigation and manipulation is: use Habitat for navigation policy development and high-level task planning, then use Claru's real-world data for manipulation policy training. This leverages Habitat's speed advantage for tasks where simulation fidelity is sufficient while using real data for tasks where the sim-to-real gap is most consequential.

Claru provides the real-world data layer that makes simulation-developed policies deployable. Where Habitat offers unlimited simulated trajectories in reconstructed indoor scenes, Claru provides real demonstrations from physical robots interacting with real objects in real environments. Our data captures the sensor noise, contact dynamics, lighting variation, and material properties that simulation approximates but cannot replicate -- exactly the information policies need to bridge the sim-to-real gap.

Every Claru demonstration includes real sensor data with natural noise characteristics: RGB images with real exposure, blur, and color variation; depth from actual sensors with their characteristic noise patterns; 6-axis force/torque from calibrated wrist sensors; proprioceptive state with real joint friction and backlash; and gripper contact detection with real mechanical characteristics. This is not augmented or randomized simulation data -- it is the actual sensor distribution your robot will encounter in deployment.

Claru's data is collected in your target deployment environment -- whether that is a warehouse, factory floor, hospital, commercial kitchen, retail store, or home. The visual backgrounds, lighting conditions, object placements, and scene complexity match production conditions, eliminating the domain gap that makes Habitat-trained policies fragile in the real world. Our teleoperators work on your physical robot platform, so action labels, kinematics, and sensor configurations match your deployment hardware exactly.

All data ships under a single commercial license with full IP assignment -- no CC BY-NC restrictions on scene data. Delivery formats include RLDS, HDF5, zarr, and LeRobot. For teams using a sim-then-real pipeline, Claru data provides the final real-world fine-tuning stage that converts simulation-pretrained policies into production-ready systems.