How to Train Embodied AI That Works Everywhere: A Universal Dataset Blueprint

In recent years, artificial intelligence technology has moved from simple "computational intelligence" to a more complex "embodied intelligence". Embodied intelligence refers to the ability to interact with the environment through a physical body (such as a robot or a virtual agent) and to achieve perception, learning, adaptation and decision-making in the process. It not only focuses on information processing, but also emphasizes the deep integration of perception, action and cognition.

 

In the fields of autonomous driving, smart home, medical care, industrial automation, etc., embodied intelligence is subverting the traditional model. However, the training of the embodied intelligence "brain" is highly dependent on multimodal interaction data obtained from the real physical environment. How to solve the problem of lack of data in the physical world has become a key bottleneck that needs to be broken through in the current evolution of embodied intelligence technology.

 

What are the biggest challenges in acquiring embodied intelligence data?

 

Data collection is expensive

Acquiring high-quality data in the field of embodied intelligence faces the challenge of high costs. The current mainstream data collection methods include teleoperated robot data, simulated synthetic data, human motion capture data, and Internet image data. Among them, teleoperated data has the highest quality, but the equipment investment and labor costs are extremely high, and it is difficult to scale. Simulated synthetic data has a lower cost, but there is a gap with the real world, and a slight deviation in parameters may cause practical application failure. Although motion capture data is accurate, it needs to be adapted to the robot configuration later. Although the Internet data is large in volume, it is mostly low-quality unstructured information.

 

Lack of unified data standards

At present, there is no unified data standard in the field of embodied intelligence, and the data format and data processing methods vary greatly between different scenarios, devices, and tasks. This fragmentation makes it difficult for data to be interoperable and reused, increasing the difficulty of development and waste of resources. At the same time, diverse environmental factors (such as lighting, object form, and cultural habits) further increase the complexity of data integration and limit the generalization ability of the model.

 

Dynamic interaction data is scarce

Dynamic interaction data is a core requirement of embodied intelligence, but it is particularly difficult to obtain. Human-machine interactions in real scenes change rapidly. Capturing these moments requires high-performance equipment, and direct acquisition in dangerous or rare scenes is almost impossible. Although simulation technology can generate virtual data, how to ensure its consistency with the real world remains a problem. The scarcity and authenticity of dynamic data have greatly restricted the development of technology.

 

Nexdata Embodied Intelligence Data Solution

 

Nexdata's off-the-shelf datasets cover key areas such as 3D models, interactive videos of people, real-time conversations, and gesture recognition. All data has been strictly screened and processed and can be purchased and used immediately. At the same time, Nexdata relies on its own professional collection base, equipped with advanced multi-modal collection equipment, to support efficient acquisition of various embodied intelligence data in complex and diverse scenarios in the real world.

 

 

116,048 Sets - 3D Handpose Dataset

 

This dataset contains 116,048 sets of 3D handpose data, each set includes hand mask image(RGB, 24-bit), depth image(16-bit), camera intrinsic parameter file(TXT), 3D keypoints file(OBJ), mesh file(OBJ), gesture type file(TXT), keypoints demo image(JPG), and mesh demo image(JPG). The data is collected indoors, with the right hand (no handheld objects), covering both first-person and third-person perspectives, multiple gesture types, finger poses, hand overall rotation poses, individuals and Kinect devices used. This dataset does not include personally identifiable facial information, with hand mask images and depth images aligned.

 

Use Case: First-person video collection of household robots

 

Demand background: A well-known household robot manufacturer hopes to develop intelligent robots that can adapt to complex household environments. They need to accurately perceive the environment, plan paths and achieve natural interaction. To this end, the customer needs a high-quality data set covering multi-modal data such as vision and audio for machine learning for the commercialization of household robots.

 

Project Difficulties: Household scenes are diverse and change frequently. The data needs to cover various indoor scenes such as apartments and office buildings, different lighting, layouts, and user postures and behaviors. Behaviors need to cover daily life behaviors, falls, quarrels and various abnormal scenes. At the same time, privacy compliance must be ensured to avoid sensitive information leakage.

 

Solution: Nexdata's professional collection team quickly finalized the collection site and customized the design of various indoor life scenes. At the same time, high-precision equipment is used to synchronously collect multimodal data, and semi-automatic tools are used to achieve efficient and accurate labeling. All data is anonymized, strictly abides by privacy regulations, and provides customers with compliant and high-quality data support.

 

The lack of embodied intelligence data has become an industry consensus. In addition to the construction and expansion of ready made data sets, Nexdata is actively building embodied intelligent robot data collection bases and simulating application scenarios to provide companies with one-stop data solutions such as off-the-shelf data and customized data collection, helping companies improve development efficiency and technology implementation capabilities.