How hot is the humanoid robot and Embodied AI sector in 2026?

In the first five months alone, the domestic sector saw 280+ funding rounds, raising over 46B RMB, with mass production of various humanoid robot products accelerating.

Industry consensus is clear: high-quality real-world ground-truth data is the core fuel for algorithm iteration and skill generalization. To achieve scalable data collection and accumulation, high-precision optical mocap is the indispensable underlying data infrastructure.

Yet behind the booming industry, R&D teams face a common dilemma: as the core challenge shifts from "can it move" to "can it generalize and scale," high-quality ground-truth data remains in short supply.

01丨  Four Data Collection Pain Points – How Many R&D Teams Are Stuck?

1. Simulation data: looks good, falls short

Many teams rely on simulation data for pre-training, but it fails to replicate real-world physical variables like friction and occlusion. The gap between simulated and real data is inherent, and data quality varies widely.

2. Insufficient breadth and precision

Complex skills like fine manipulation and human-robot interaction require visual, motion, force, and tactile data. Yet most solutions lack key modalities like tactile and force feedback, and precision falls short of supporting fine-grained control learning. Models end up learning only simple, repetitive motions – ill-suited for complex real-world scenarios.

3. In-house data lines: high cost, low return

Building a data collection line from scratch requires heavy investment in space, equipment, personnel, and workflows. Industry cost per hour can reach hundreds of RMB, pushing 100M+ for a million-hour dataset. Add fragmented toolchains – collection, labeling, QC are siloed – and teams often end up with "high input, slow output, hard to scale."

4. Robot-bound data: hard to reuse

Sensor layouts, interface protocols, and data formats vary widely across robots. Data collected for one device or task often can't transfer across brands or models. Data assets can't accumulate, and economies of scale remain out of reach.

Where's the problem? One industry gap:

A lack of end-to-end data collection solutions covering acquisition, processing, labeling, and management – with high precision, multimodality, reusability, and scalability.

02丨  CHINGMU Data Factory: Building Universal Data Collection Infrastructure for Embodied AI

To address these pain points, CHINGMU leverages optical mocap technology to build a Data Factory solution – capturing multimodal data from humans, robots, objects, and environments, creating an end-to-end data production pipeline that delivers scalable, reusable, and transferable real-world data for Embodied AI.

▶ Multimodal synchronized acquisition – making data truly reliable

CHINGMU's Project Decode multimodal high-quality Embodied AI data acquisition system integrates optical, inertial, and tactile sensing – breaking the limits of single-modality motion data. It synchronously captures sub-mm-precision motion, video, tactile, object, and environmental data in real-world scenarios. Paired with the PulseHand optical-inertial fusion glove – 15 optical markers + 11 IMUs per hand, restoring 25 DoF finger movements, with built-in high-resolution e-skin for fine tactile sensing – it captures everything from precision grasping to complex gestures.

All output data serves as high-precision ground-truth benchmarks, providing accurate training labels and error validation for robot kinematics modeling, sensor calibration, SLAM optimization, and closed-loop control – forming a complete "acquisition → training → validation → optimization" data-driven R&D loop.

▶ µs-level sync + AI-driven auto-alignment – 80% efficiency boost

To solve the multi-source data alignment bottleneck, the system applies µs-level timestamp unification across the entire pipeline after production-integrated acquisition, with AI-driven auto-repair and alignment – eliminating manual cleanup entirely.

▶ Full-stack service – no need to build from scratch

CHINGMU provides end-to-end services from site survey & design, equipment selection & deployment, protocol development, to raw data processing, QC, and labeling – delivering one-stop solutions. Clients can obtain standardized, scalable data production capabilities without coordinating multiple vendors or building in-house collection/labeling teams – drastically reducing technical barriers, time, and labor costs.

The solution also supports customized scenario adaptation and enterprise-grade private dataset construction – flexible enough for industrial packing, home services, retail sorting, large-space group collection, and more.

▶ Standardized cross-platform transfer – making data assets portable

With standardized data formats and interfaces, collected data can transfer to different models across major robot brands – breaking single-device lock-in. Capture once, reuse across platforms.

03丨  Data Asset Accumulation: From Custom Services to Ecosystem Co-building

As a professional Embodied AI data infrastructure provider, CHINGMU's capabilities go beyond building dedicated data lines – with proven validation across multiple sectors.

In Embodied AI, CHINGMU has built the MotionDecode dataset – a multimodal high-quality motion data system for robot enterprises, algorithm teams, research institutions, and simulation platforms. Currently at 3,000 hours built, with an estimated annual output of 500,000 hours, it covers body pose, hand pose, ego video, tactile pressure, IMU, third-person view, interactive object pose, and environmental data – across home, industrial, office, and outdoor scenarios – compatible with 10+ standard formats including BVH, FBX, and CSV.

Additionally, CHINGMU, together with Tsinghua University and Shanghai Film Art Academy, launched China's first professional dance database – officially listed on the Shanghai Data Exchange. Captured entirely with CHINGMU's high-precision optical mocap system and proprietary CMAvatar software, it covers classical Chinese dance, folk dance, and more. To date, the database has accumulated 4,200+ video clips with AI-annotated tags and massive high-quality human motion data – directly supporting digital human animation, AI model training, film, and game content creation.

China's first professional dance database

Scalable Embodied AI deployment is never about a single algorithm breakthrough – it's about a full-chain, system-level capability from data collection to model training to algorithm validation to real-world deployment. Data, as the core fuel for algorithm iteration – its quality, scale, and reusability – determines the ceiling of robot intelligence.

As real-world training policies advance and mass deployment accelerates, the value of data infrastructure will only grow. CHINGMU will continue to leverage high-precision optical mocap as its core, building scalable, reusable real-world data production capacity – and working with industry partners to drive Embodied AI from "prototype breakthrough" to "mass deployment."

Decode the real world. Train next-gen Embodied AI. It starts now.