RoboCasa Alternative: Real-World Training Data for Household Robotics

RoboCasa represents the frontier of simulated household robotics data, with its AI-generated kitchen environments and 100K+ demonstrations pushing the boundaries of what procedural generation can achieve. Its 2,500+ scene configurations provide unprecedented layout diversity for pretraining household manipulation policies. However, the gap between simulated kitchens and real ones is profound: real kitchens have wet surfaces, deformable foods, transparent containers, reflective appliances, and lighting that changes with the time of day. These elements are not cosmetic details -- they fundamentally affect how robots must perceive and interact with kitchen environments. Claru bridges this gap by collecting demonstrations in actual kitchens on your physical robot. Our teleoperators work with real food items, real liquids, real appliances, and real kitchen layouts, capturing the contact dynamics, visual complexity, and material properties that simulation cannot reproduce. For teams that have validated their approach on RoboCasa, Claru provides the real-world fine-tuning layer that converts simulation performance into production reliability. We deliver synchronized multi-modal data including force/torque sensing critical for tasks like pouring, washing, and handling fragile items, in formats compatible with RoboCasa's robomimic training ecosystem.

RoboCasa is a large-scale simulation benchmark for everyday household robot manipulation, developed by Yuke Zhu's lab at UT Austin in collaboration with NVIDIA Research. Published in 2024, RoboCasa provides over 100,000 teleoperated demonstrations across 100 kitchen manipulation tasks in 2,500+ procedurally generated 3D scene configurations. It was built on top of the robosuite simulation framework with the MuJoCo physics engine, and represents the most ambitious effort to date to create diverse simulated environments for household robotics.

RoboCasa's distinctive contribution is its use of generative AI to scale environment diversity. The project employed text-to-3D and text-to-image pipelines (including Objaverse assets and AI-generated textures) to create over 150 unique 3D object categories, thousands of object instances, and hundreds of kitchen layouts with varying floorplans, cabinet styles, appliances, and decorative elements. This AI-augmented approach to scene generation aimed to narrow the sim-to-real gap through sheer visual diversity rather than photorealism alone.

Tasks span composite kitchen activities: retrieving items from cabinets and drawers, using appliances (microwave, coffee machine, stove), washing and drying, preparing simple meals, and clearing countertops. Each task is parameterized over kitchen layout, object placement, and object instance, generating the 2,500+ unique scene configurations. Demonstrations were collected from human teleoperators through a VR interface controlling a simulated Franka Emika Panda arm.

RoboCasa is released under the MIT License and includes integration with the robomimic learning framework. The dataset provides RGB-D observations from two cameras (agentview and robot eye-in-hand), 7-DoF proprioception, end-effector pose, gripper state, and language task descriptions. The project also provides a simulation environment for generating additional demonstrations and evaluating learned policies.

RoboCasa's AI-generated environments, while visually diverse, are not photorealistic. Objaverse assets and synthesized textures create scenes that look recognizably synthetic -- flat surfaces, uniform material properties, and missing fine details like food stains, water droplets, or cabinet scratches. Real kitchens have visual complexity that procedural generation does not capture: worn wooden surfaces, translucent containers, reflective stainless steel, and steam from cooking. Policies trained on RoboCasa's visual distribution overfit to synthetic aesthetics and struggle with real kitchen visuals.

MuJoCo's rigid-body physics cannot simulate the materials that dominate kitchen manipulation. Pouring water, handling wet dishes, folding towels, spreading butter, and washing vegetables all involve fluid dynamics, deformable materials, or surface adhesion that rigid-body simulators approximate poorly or not at all. RoboCasa's kitchen tasks are constrained to what rigid-body simulation can handle, which excludes many of the most common and challenging real kitchen operations.

The dataset uses a single simulated Franka Panda arm. Real household robots are predominantly mobile manipulators (e.g., Hello Robot Stretch, Toyota HSR, Agility Digit) with different kinematic chains, workspace envelopes, and mobility constraints. A fixed-base arm in simulation does not learn the navigation-manipulation coordination, base-placement planning, or whole-body collision avoidance that mobile household robots require.

RoboCasa's demonstrations are collected via VR teleoperation of a simulated robot, which introduces a subtle but important gap. VR teleoperation in simulation has no haptic feedback, no physical resistance, and no weight perception. Operators learn to rely purely on visual cues, producing trajectories that do not reflect the force-modulated movements needed for real kitchen tasks like cracking eggs, pressing buttons, or turning knobs. Real-world demonstrations with haptic feedback produce fundamentally different -- and more useful -- trajectories.

While 100K demonstrations is large by simulation standards, the demonstrations are distributed across 100 tasks and 2,500+ scene configurations, yielding on average only about 40 demonstrations per scene. Production policies for specific kitchen environments often need hundreds of demonstrations per task variant to achieve the reliability expected in consumer or commercial deployment.

RoboCasa is ideal for developing and evaluating kitchen manipulation algorithms in simulation before committing to physical hardware. If your research focuses on long-horizon task planning in household settings, learning from diverse kitchen layouts, or evaluating generalization across scene configurations, RoboCasa provides the scale and variety needed for meaningful experiments. Its 100-task scope is broader than most kitchen manipulation benchmarks.

RoboCasa is also useful for pretraining household manipulation policies. Exposing a policy to thousands of kitchen configurations during pretraining can teach spatial priors about kitchen layouts, cabinet opening mechanics, and appliance interactions that transfer partially to real settings. This pretraining value is highest when combined with real-world fine-tuning.

Choose Claru when you are building for deployment in physical kitchens -- whether residential, commercial restaurant, or industrial food preparation. Real kitchens present challenges that simulation cannot capture: reflective surfaces confuse depth sensors, wet objects slip, food items deform unpredictably, and lighting changes throughout the day. Claru collects data in real kitchens with real objects, real liquids, and real lighting conditions on whatever robot platform you plan to deploy.

Claru delivers the real-world kitchen data that makes RoboCasa-pretrained policies deployable. Where RoboCasa generates procedural kitchen environments in simulation, Claru sends trained teleoperators into actual kitchens -- commercial, residential, or laboratory -- to collect demonstrations with real objects, real materials, and real physics. The resulting data captures the visual texture, contact dynamics, and material properties that simulation cannot replicate.

For teams developing household robots, Claru addresses RoboCasa's most critical gap: deformable and fluid interactions. We collect demonstrations of tasks involving liquids (pouring, washing), fabrics (folding, wiping), and compliant objects (fruit, bread, packaging) that RoboCasa's rigid-body physics cannot simulate. These demonstrations include force/torque data that captures how skilled operators modulate grip and contact forces for these challenging materials.

Claru supports mobile manipulators and other household robot platforms that RoboCasa's fixed-base Franka cannot represent. We collect demonstrations that include navigation, base positioning, and whole-body coordination alongside manipulation, providing the full behavioral repertoire that household robots need.

Data is delivered in your preferred format (RLDS, HDF5, zarr, or LeRobot) with multi-camera RGB-D, synchronized force/torque, and validated language annotations. For teams combining RoboCasa pretraining with Claru fine-tuning, we ensure schema compatibility so the transition from simulation to real-world data is seamless.