In 2026, humanoid robots enter the inaugural year of 10,000-unit mass production. Langhui Technology deeply engages in embodied AI data services, building 15 high-quality datasets across four major domains—multimodal perception, robot actions, scene data, and simulation environments—with 1M+ action sequences, 500K+ scene records, and 100+ embodiment types, providing high-quality, multimodal, cross-embodiment training corpora for general robot models such as OpenVLA and GR00T.
From Figure 03 to Optimus Gen 3, from OpenVLA to GR00T, human robotics began a new era of Physical AI.
Figure AI launches its third-generation humanoid robot Figure 03, designed for daily home service. It can perform complex chores such as folding laundry, dishwashing, and tidying rooms. Its soft fabric hand design improves manipulation precision, with 5-hour battery life, and has entered home-scenario pilot deployment.
Tesla's Optimus third-generation robot kicks off 10,000-unit mass production, with 2026 full-year output expected to exceed 100,000 units, covering industrial handling, warehouse sorting, and home service scenarios. Neural-driven control + end-to-end VLA models achieve millimeter-level operation precision.
Stanford releases the open-source OpenVLA vision-language-action model, trained on the Open X-Embodiment dataset, supporting cross-embodiment zero-shot transfer with 5× fine-tuning efficiency, and has become the foundational model standard for embodied AI research.
NVIDIA launches the Rubin GPU platform and GR00T 2.0 humanoid robot foundation model. Isaac Sim simulation supports 10M-scale scene generation, with a 92% sim-to-real transfer success rate, becoming the standard infrastructure for Physical AI development.
Physical Intelligence introduces the π0 general robot policy model, validating generalization across 100+ robot embodiments. A single model covers four major task categories—grasping, assembly, navigation, and collaboration—pioneering a new Physical AI paradigm.
MIIT data shows China's humanoid robot production in 2026 is expected to exceed 100,000 units, a 5× increase over 2025. With 140+ domestic OEMs and 330+ released products, the industry has officially entered the stage of scaled mass production and deployment.
Covers four broad areas of multimodular perception, robotic action, scenario data, simulation environment, providing high-quality, cross-book, multi-modular training language for universal robotic models such as OpenVLA, GR00T
Multi-sensor fusion data including vision, tactile, proprioception, depth, and LiDAR, covering RGB-D, point cloud, force-torque, IMU and other modalities.
Robot action sequence data for grasping, manipulation, navigation, and collaboration, including joint angles, force-torque, trajectories, and execution result annotations.
Diverse scene data for homes, factories, offices, outdoors, and retail, including object pose, environment layout, and task goal annotations.
10M-scale scene generation, digital twin, and edge-case data based on NVIDIA Isaac Sim and the GR00T platform, achieving a 92% sim-to-real transfer success rate.
Quality indicators for the core of Langhui ' s smart data infrastructure
Cover the dominant shape, mechanical arm, mobile robotic book. Body
Expert clearance + automated quality control dual assurance
Home/workshop/office/outdoor/retail wide coverage
Physical grade rendering + remokinetic simulation
The four main areas of focus multimodular perception, robotic action, scene data, simulation of the environment, and the construction of smart AI training data base
RGB-D vision data with object recognition, pose estimation, depth map, and semantic segmentation annotations, supporting robot visual perception model training.
Robot tactile sensor data with pressure distribution, texture recognition, slip detection, and force-torque feedback annotations, supporting dexterous manipulation.
3D depth maps and point cloud data, with scene reconstruction, object geometry, distance measurement, and spatial layout annotations.
LiDAR point cloud data with 3D object detection, SLAM mapping, obstacle recognition, and scene flow annotations.
Robot grasping action sequences with grasp pose, force-torque curves, success/failure labels, and object property annotations.
Robot fine manipulation sequences including assembly, insertion/removal, twisting, and cutting actions, with joint trajectories and execution result annotations.
Mobile robot navigation trajectory data with path planning, obstacle-avoidance decisions, SLAM mapping, and target-point navigation annotations.
Human-robot collaboration and multi-robot collaboration data with intent recognition, action synchronization, safety constraints, and task allocation annotations.
Home scenes such as living rooms, kitchens, bedrooms, and bathrooms, with object pose, furniture layout, and task goal annotations.
Industrial scenes such as production lines, warehouses, and assembly workshops, with equipment layout, material pose, and process flow annotations.
Office scenes such as workspaces, meeting rooms, and reception areas, with document organization, beverage service, and reception guidance task annotations.
Outdoor and retail scenes such as streets, campuses, supermarkets, and shelves, with dynamic obstacle, product recognition, and navigation path annotations.
Omniverse-based digital twin scenes of factories, homes, and warehouses, with physics-level rendering + rigid-body dynamics simulation.
Procedurally generated diverse simulation scenes with randomized objects, lighting, and textures, supporting large-scale pre-training.
Simulation of rare, hazardous, and adversarial edge cases with collision, failure, and abnormal-condition annotations, enhancing robustness.
Credible data sets, customized data services, network of certified experts to build full-link data solutions from standardization to customization
15 ready-to-use embodied AI datasets covering four major domains—multimodal perception, robot actions, scene data, and simulation environments—with standardized multimodal formats supporting VLA model training.
Industrial-grade annotation platform with on-demand robot data collection and annotation solutions, supporting vision, action, scene, and simulation customization with industrial-scale capacity.
Expert review network in robotics and embodied AI, partnering with university labs and robotics engineers to ensure data professionalism and cross-embodiment consistency.
Six typical AI applications based on lang Hui's smart data set
Providing VLA model training data for humanoid, industrial, and service robots, supporting dexterous manipulation and autonomous decision-making.
Providing perception, decision-making, and planning training data for autonomous driving systems, covering complex road scenarios and edge cases.
Intelligent inspection and defect recognition: robotic arms with vision systems enable high-precision inline quality inspection, with a 10× efficiency boost.
Training data for warehouse and sorting robots, covering the full logistics workflow of handling, sorting, and palletizing.
Training data for housework and companion robots, supporting cleaning, cooking, caregiving, and other home service tasks.
Training data for surgical and rehabilitation robots, with high-precision action sequences and safety constraint annotations.
Contact Langhui Data Specialist to obtain complete smart dataset catalogues, test privileges and customized solutions