Robo.ai and DaBoss.AI Form Joint Venture to Build Distributed Embodied AI Data Platform

2026-02-10 11:31:38 ET

Robo.ai Inc. ( NASDAQ: AIIO) and DaBoss.AI Inc., a Silicon Valley-based embodied intelligence data technology company, announced they have signed a definitive joint venture agreement to establish a distributed embodied AI data acquisition and annotation platform in the United Arab Emirates. The initiative aims to help AI developers tackle some of the most pressing challenges facing advanced artificial intelligence systems that operate in the physical world. ( Finviz )

Addressing Core Challenges in Physical AI Data

The new joint venture -- majority-controlled by Robo.ai, which will hold a 51 % equity stake and appoint most of the board -- plans to build a global distributed network that collects and labels large-scale embodied intelligence data essential for training AI models. By consolidating this data into high-quality, multi-modal datasets, the partners hope to reduce the high cost, complexity, and compliance barriers currently facing embodied AI development.

Embodied data -- information that reflects real-world physical actions, motion, and interactions -- is critical for AI systems that are intended to function in real environments, whether in robotics, autonomous systems, or advanced machine-learning applications. However, acquiring such data has traditionally been capital-intensive and logistically challenging, involving complex robotics hardware and diverse environments.

Dual-Engine Data Collection Approach

To address these challenges, the joint venture will implement a dual-engine data acquisition methodology:

• Embodied Data Collection: Controlled environments using standardized robotic terminals to capture precise action trajectories and force feedback.
• Non-Embodied Capture: Global VR and wearable devices collect first-person visual and motion data across diverse real-world scenarios.
  This mix of controlled and scalable data sources aims to produce high-fidelity datasets that can improve AI model training across varied operational contexts.

Key Strategic Goals

The partnership will focus on three broad objectives:

1. Enhancing Delivery Efficiency: By creating a cross-geography global network that can respond to data acquisition tasks quickly and cost-effectively.
2. Access to Diverse Scenarios: Enabling AI developers to obtain multi-modal datasets from different environments and conditions around the world.
3. Standardized Compliance: Leveraging the UAE's strategic position to develop internationally aligned data processing frameworks that help ensure legal and ethical compliance.

The venture aims to complete the initial deployment of this distributed network and begin commercial operations within 90 days of the agreement's execution -- a rapid timeline aimed at meeting the urgent data needs of organizations developing embodied AI systems.

Executive Insights

Aiden Zhu, Co-founder and U.S. CEO of DaBoss.AI, said the collaboration will transform robotic hardware into elastic, scalable global data infrastructure, removing many of the financial and logistical barriers that have traditionally slowed embodied data acquisition.

Benjamin Zhai, CEO of Robo.ai, added that the partnership will provide the company and its clients with access to high-quality physical world data and cutting-edge acquisition technology, enabling a strategic transition of AI models from purely digital environments into the physical world.

Why It Matters

As AI systems increasingly move beyond purely virtual tasks into real-world environments -- including robotics, autonomous navigation, and machine human interaction -- the demand for rich, high-quality data that reflects real physical behavior has surged. Datasets that capture embodied intelligence are seen as a critical backbone for these technologies to achieve scalability, safety, and reliability.

By combining Robo.ai's global network capabilities with DaBoss.AI's expertise in intelligent data technologies, the joint venture seeks to standardize, scale, and streamline the collection of this data. If successful, the platform could become a key resource for AI developers worldwide, accelerating the pace at which machine intelligence bridges the gap between digital prediction and physical action.

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COMTEX_473272137/2927/2026-02-10T11:31:38