Langhui Technology's Embodied AI / Physical AI data services focus on LiDAR point cloud 3D annotation, multi-camera sensor fusion, robot demonstration trajectory collection, and world model rollout data. From perception to decision-making to execution, we deliver end-to-end data support for VLA models, humanoid robots, industrial arms, and service robots—accelerating the 2026 mass-production era for humanoid robots.
Staying aligned with the Physical AI research pipelines of leading global robotics labs—including Figure, Tesla, NVIDIA, and Physical Intelligence—Langhui's expert team continuously transforms the latest embodied intelligence breakthroughs into scalable, production-grade training and evaluation data.
In 2026, Figure 03 officially entered commercial deployment in home scenarios. Powered by the Helix VLA model, it achieves end-to-end control from voice commands to bimanual operation—completing long-horizon household tasks such as cooking, organizing, and cleaning. Langhui provides matching home-scenario operation trajectories and multimodal instruction data.
Tesla Optimus Gen 3 broke through 10,000-unit mass production in 2026, with per-unit cost compressed to the $20,000 range. Gen 3 introduces an all-new dexterous hand and end-to-end neural network control, reaching commercial-grade performance on factory handling and assembly tasks. Langhui provides factory operation trajectories and failure case data.
OpenVLA, jointly released by Stanford and Berkeley, has become the new open-source embodied intelligence benchmark. The 7B-parameter model outperforms closed-source solutions such as RT-2 on the SIMPLER and RealxArm benchmarks. Langhui provides large-scale teleoperation demonstration data and cross-body transfer sets aligned with it.
NVIDIA's GR00T N1 general robot foundation model and the Rubin computing platform together form the Physical AI infrastructure. The Newton physics engine and the GR00T Mimic data generation pipeline boost synthetic data efficiency by 10×. Langhui provides real-synthetic hybrid training data services.
Physical Intelligence released π0.5, a universal robot policy that achieves zero-shot transfer across bodies and scenarios. From folding laundry to assembling cartons, a single model completes 100+ real-world tasks—foreshadowing the path to VLA generalization. Langhui provides cross-body demonstration data and evaluation services.
China's MIIT "Guiding Opinions on the Innovative Development of Humanoid Robots" landed in 2026, explicitly targeting 10,000-unit mass production in 2026 and 100,000-unit scale by 2027. Beijing, Shanghai, and Shenzhen simultaneously opened scenario validation. Langhui deeply serves the data needs of domestic robot body manufacturers.
Four core capabilities spanning the entire Physical AI data lifecycle—from perception-layer 3D annotation to decision-layer world model rollout—building a productionizable, auditable, and traceable embodied intelligence data pipeline.
High-precision 3D annotation services for LiDAR scenarios, covering 3D bounding boxes, instance segmentation, semantic segmentation, multi-frame tracking, free-form shapes, and more. Supports 64/128-line mechanical and solid-state hybrid radar data—adaptable to autonomous driving and service robot perception training.
Vision + depth + IMU + tactile multimodal sensor fusion annotation services, solving the challenge of spatiotemporal alignment across multi-view robot body data. Supports time-sync and spatial calibration for heterogeneous data such as RGB-D, stereo vision, event cameras, and torque sensors.
Teleoperation + demonstration data collection services, covering four mainstream paradigms: VR teleoperation, bimanual master-slave control, motion capture demonstration, and Kinesthetic guidance. Records full-dimensional data including joint angles, end-effector poses, torques, vision, and tactile—aligned with Open X-Embodiment standards.
Physical world simulation and rollout data production services, based on simulation engines such as NVIDIA Isaac, MuJoCo, and Genesis to generate action-conditioned video prediction, counterfactual scenarios, and long-tail boundary data—bridging the gap where real-world collection is costly and coverage is insufficient.
Six ready-to-use standard embodied AI dataset products covering operation trajectories, point cloud annotation, multimodal fusion, humanoid behavior, scene understanding, and world model dimensions—delivered via both subscription and customization models, aligned with the Open X-Embodiment international standard.
Human demonstration trajectories covering real-world operation tasks such as grasping, placing, assembly, cooking, and cleaning. Includes joint angles, end-effector poses, vision, torque, and tactile full-dimensional data—aligned with Open X-Embodiment cross-body standards.
LiDAR point cloud annotation datasets across urban roads, indoor scenes, and industrial parks. Includes 3D bounding boxes, semantic segmentation, instance segmentation, and multi-frame tracking—supports 64/128-line mechanical and solid-state radars.
Multimodal fusion datasets from 6-camera surround + depth + IMU + tactile on robot bodies. Includes time-sync annotation, spatial calibration parameters, and cross-modal semantic alignment labels—adaptable to VLA model multimodal training.
Whole-body behavior datasets for humanoid robots, covering bimanual coordination, gait balance, whole-body dynamics, human-robot interaction, and fall recovery. Spans home, office, and factory scenarios—supports mainstream bodies such as Figure, Optimus, and Unitree.
Visual-language-3D joint annotation datasets for robot scene understanding, including scene graphs, object relations, affordances, spatial layouts, and interactive regions—supports open-vocabulary object detection and scene reasoning training.
Action-conditioned video prediction and world model training data generated by simulation engines, including counterfactual scenarios, long-tail boundaries, physical interactions, and deformation simulations—supports Sora, Genie, V-JEPA-class world model pretraining.
From humanoid robots to industrial arms, from service robots to autonomous driving—Langhui's Physical AI data services adapt to the full spectrum of embodied intelligence deployment scenarios, providing tailored data solutions for different robot bodies.
Behavior data for bimanual coordination, gait balance, household operations, and human-robot interaction for humanoid robots such as Figure, Tesla Optimus, Unitree H1/G1, Zhiyuan, and Unitree—empowering VLA models toward generalization across home and office scenarios.
High-precision operation trajectory data for assembly, handling, welding, coating, and quality inspection for industrial arms such as UR, KUKA, FANUC, and ABB—supporting few-shot rapid adaptation for flexible manufacturing and lights-out factory scenarios.
Scene understanding, semantic navigation, human-robot dialog, and object recognition data for delivery, cleaning, guide, distribution, and hotel service robots—empowering the intelligent upgrade of service robots in commercial and home scenarios.
LiDAR point cloud, multi-camera fusion, BEV perception, occupancy networks, and end-to-end planning data annotation services for L2+/L3/L4 autonomous driving systems—covering urban NOA, highway pilot, and parking scenarios.
Langhui has built a quality assurance system that runs through the entire embodied AI data production pipeline. From body coverage to operation precision, scene realism, and trajectory completeness—every dimension is backed by quantifiable KPIs, benchmarked against the international standards of Appen, Scale AI, and Labelbox.
Partner with Langhui's experts to inject the latest 2026 Physical AI research into your robots. Whether you're a humanoid robot, industrial arm, service robot, or autonomous driving team, we tailor end-to-end data solutions spanning perception annotation to world models.