The Robot Data Industry: Pain Points, Solutions, and Future Trends

The robot industry is experiencing rapid growth, yet it faces a critical bottleneck: the lack of high-quality, professional data. As robots evolve from single-machine intelligence to integrated systems capable of perception, decision-making, and planning, the demand for reliable, diverse, and scalable data has become increasingly urgent. This article delves into the current state, core challenges, technical solutions, market dynamics, and future prospects of the robot data industry, based on insights from industry research and practical cases.

Industry Background: The Data Dilemma Constraining Robot Development

Robotics development relies heavily on a full-stack technical ecosystem, encompassing mechanical components (motors, reducers), general kits, perception/decision modules, AI algorithms, and operating systems (RTOS, ROS, multi-machine scheduling OS). However, the industry grapples with two core pain points related to data:

Acute Shortage of Professional Data

The robot industry suffers from a severe lack of specialized data characterized by large volume, poor structure, chaotic scenes, and diverse modalities. Unlike general AI fields, robot data must align with real-world application scenarios, involving multi-modal interactions (visual, tactile, linguistic, laser radar), dynamic environmental changes, and complex task logic. Existing public datasets—such as Meta’s Habitat (focused on 3D environmental modeling with 1K scenes), Stanford’s BEHAVIOR (covering 9K objects and 50 scenes for task arrangement), and Google Deepmind’s OPEN X-Embodiment (with 160K tasks and 1M real-robot samples)—remain fragmented. They fail to meet the comprehensive needs of robot pre-training and fine-tuning, as the total known trajectory data in the industry is less than 2 million, a far cry from the 100 million-level requirement for large-scale model training.

Inefficient Data Utilization

Existing data resources face issues of scattered management and poor versatility. Public datasets often exceed 4000GB, but they are concentrated in fixed robot forms (e.g., mechanical arms) and lack uniform standards for multi-modal alignment. This makes data cleaning, annotation, and reuse extremely difficult. Additionally, most enterprises lack the technical capability to build closed-loop data systems, resulting in isolated data silos that cannot effectively feed back into robot algorithm optimization and hardware iteration.

Core Requirements for High-Quality Robot Data

To support the development of versatile and reliable robots, data must meet four key requirements:

Scale and Coverage: Trajectory data must reach the 100 million-level, with uniform distribution across diverse scenes (indoor, outdoor, industrial, service) to avoid bias in model training.
Multi-Modal Alignment: Integration of visual (images, videos, multi-view), tactile, linguistic, laser radar, and millimeter-wave data, with precise temporal and spatial alignment to simulate real-world robot-environment interactions.
Versatility and Extensibility: Data should be based on meta-objects, meta-actions, and meta-scenes—standardized abstractions of objects, actions, and environments-to support cross-robot and cross-scene applications. For example, meta-actions include trajectory reconstruction and end-contact point marking, while meta-scenes require semantic annotation.
Closed-Loop Usability: A complete data chain from collection, cleaning, and annotation to augmentation, model training, and deployment, enabling iterative optimization of robot systems.

Technical Solutions: Building a Full-Stack Robot Data Ecosystem

To address these challenges, the industry has developed a full-stack technical solution covering data collection, processing, management, and model training, with a focus on automation, distribution, and customization.

Data Collection: Diversified Sources and Customized Factories

Data collection combines public data refinement, large-scale crawling, and custom data factories to ensure both volume and quality:

Public Data Refinement: Screening and extracting valuable information from terabytes of domestic and foreign public data (e.g., Ros Bag files, video demonstrations) through customized algorithms to filter out low-quality and irrelevant content.
Large-Scale Crawling: Using web crawlers to gather multi-modal data (images, videos, text) from public sources, supplemented by web3-based global data collection networks-with hundreds of thousands of data collectors worldwide-to quickly acquire customized scene data.
Custom Data Collection Factories: Establishing specialized facilities with high-precision equipment, such as the Robot Training Technology Innovation Center at Hongkong Laboratory. These factories are equipped with infrared high-precision indoor positioning systems and high-definition video collection systems, covering security, low-altitude planning, and ground mobile service scenarios. For example, in unmanned retail scenarios, they collect data throughout the entire process from greeting to payment, forming a closed loop of data feedback.

Data Processing: Automation and Multi-Modal Enhancement

Data processing focuses on solving problems of noise, misalignment, and scarcity through automated tools and augmentation techniques:

Preprocessing and Alignment: Using multi-modal alignment annotation tools to integrate data from different sources (e.g., VICON motion capture, structured light, laser radar) and standardizing it via Protobuf serialization for unified storage and processing.
Automated Cleaning and Annotation: Leveraging AI tools for semantic labeling, path annotation, and intent annotation, reducing manual labor. For example, generating Affordance Heatmaps to mark object interaction points and using trajectory reconstruction to standardize meta-actions.
Data Augmentation: Expanding data volume and diversity through techniques like scene generation, incremental iteration, and cross-scene adaptation, addressing the scarcity of high-quality real-world data.

Data Management: Distributed Platforms and Data Lakes

A highly automated distributed data management platform is the core of efficient data utilization:

User-Controllable Workflows: Based on Jenkins for process customization, supporting user-defined scene descriptions and screening strategies to adapt to diverse business needs.
Distributed Screening and Processing: Using in-memory databases like Spark for large-scale data filtering, splitting raw Ros Bag files into serialized segments, and classifying them into data lakes with semantic labels.
Unified Storage and Indexing: Building data lakes to store indexed multi-modal data (Ros Bag, images, motion capture data), enabling fast retrieval and cross-type adaptation. The platform supports 10,000-card distributed analysis, simulation, training, and testing, ensuring efficient processing of massive datasets.

Model Training: Distributed Computing and Embodied Large Models

Data-driven model training relies on distributed computing frameworks and specialized embodied intelligence models:

Distributed Training Middleware: Supporting cluster collaboration and distributed training to handle large-scale embodied data, with dedicated training factories providing packaged computing resources for clients.
General and Dedicated Models: Developing general embodied large models and scenario-specific dedicated models to address the limitations of traditional language multi-modal models (e.g., those from Baichuan AI, Moonshot AI) in robot applications. These models integrate perception, decision-making, planning, and safety modules to meet the unique requirements of robot systems.

Market Dynamics: Growth Drivers and Competitive Landscape

Market Size and Growth Trends

The robot data industry is fueled by the booming AI training and robot markets:

The global AI training preparation and management market reached $5.5 billion in 2023, with a compound annual growth rate (CAGR) of 19%, expected to hit $11 billion by 2027.
2023 is regarded as the embodiment year, attracting $12 billion in global investment. As embodied intelligence technology matures, the demand for professional robot data is expected to kick off with $300 million in 2025, with strong growth momentum.

Cost Structure of Data Collection

Data collection involves significant investment in equipment, personnel, and technology. Taking trajectory data as an example, the cost breakdown (excluding R&D and operational costs) is as follows:

Public Data: $50,000 per year for processing and storage, with an annual investment of $2 million to $10 million depending on data volume.
Motion Capture Data: 68 person-days to collect 0.19 million trajectories; for 50 million trajectories annually, 17 personnel and $2 million worth of NOKOV motion capture equipment are required, costing $3.4 million.
Robot Data: 112 robot-days to collect 0.14 million trajectories; for 50 million trajectories annually, 15 robots (Aloha, $200K each) and 30 operators are needed, costing $6 million.
For a total of 0.5 billion trajectories over three years, the total investment in data collection alone reaches $182 million, plus $48 million for R&D (10 engineers) and operations, totaling $230 million.

Competitive Landscape and Customer Segments

The industry consists of two main types of players:

Overseas Enterprises: Dominated by SaaS-based service providers (e.g., Roboflow, Labelbox) and data synthesis companies (e.g., Reverie), focusing on API tools and cloud-based data management.
Domestic Enterprises: Focus on customized services, such as data hosting platforms, custom data collection factories, and standard robot hardware supply. Key players include Pan Yu Zhiyuan, which collaborates with research institutions (BVAI Zhiyuan Research Institute, Shanghai Artificial Intelligence Laboratory) and enterprises (Fourier Intelligence, Zhiyuan Robotics) to provide data sets, training hosting, and custom models.

Customers are diversified, including:

Research institutions and key laboratories: Lack data collection and management capabilities but have strong demand for project-specific data.
Robot manufacturers: Focus on hardware development and lack AI brain R&D capabilities, requiring standard hardware and custom models.
Listed companies and industry leaders: Possess vertical scene data but need professional data processing and model training services.

Future Trends and Challenges

Development Goals

The long-term goal of the robot data industry is to become the “HuggingFace + ImageNet” of professional robot data establishing a standardized, open data ecosystem that provides universal data sets, development communities, and technical support for the global robotics industry. This involves building standard robot platforms (e.g., Aloha, XARM) for data collection and product deployment, and promoting the popularization of embodied intelligence technology.

Key Challenges

Standardization: Lack of unified standards for multi-modal data alignment, meta-object/meta-action definition, and data storage formats, hindering interconnection and reuse.
Cost Control: High costs of equipment, personnel, and computing resources limit small and medium-sized enterprises’ access to high-quality data.
Scene Complexity: Real-world scenes are highly dynamic and uncertain, requiring continuous expansion of data coverage and enhancement of model adaptability.

Future Directions

Open Data Ecosystem: Building a community-driven open data platform to encourage collaborative data sharing and annotation, reducing redundant investment.
AI-Powered Automation: Further improving the automation level of data collection, cleaning, and annotation through generative AI, reducing labor costs.
Edge Computing Integration: Combining edge computing with cloud data lakes to enable real-time data processing and feedback, supporting low-latency robot applications.

Conclusion

The robot data industry is a critical foundation for the development of embodied intelligence, with huge market potential and far-reaching industrial impact. Despite facing challenges such as data scarcity, high costs, and lack of standards, the industry is evolving rapidly through technical innovations in data collection, processing, and management. As more enterprises and research institutions participate in building the ecosystem, professional, standardized, and scalable robot data will unlock the full potential of robotics, driving breakthroughs in service, industrial, and special-purpose robots. The future of the robot data industry lies in balancing openness and customization, efficiency and quality, to become a key enabler of the intelligent transformation of the robot industry.