Artificial Intelligence

X-Humanoid Introduces Tien Kung 3.0 as Deployment Challenges Persist in Humanoid Robotics

A closer look at the tech, AI, and open ecosystem behind Tien Kung 3.0’s real-world push

Updated

February 18, 2026 8:03 PM

Humanoid robots working in a warehouse. PHOTO: ADOBE STOCK

Humanoid robotics has advanced quickly in recent years. Machines can now walk, balance, and interact with their surroundings in ways that once seemed out of reach. Yet most deployments remain limited. Many robots perform well in controlled settings but struggle in real-world environments. Integration is often complex, hardware interfaces are closed, software tools are fragmented, and scaling across industries remains difficult.

Against this backdrop, X-Humanoid has introduced its latest general-purpose platform, Embodied Tien Kung 3.0. The company positions it not simply as another humanoid robot, but as a system designed to address the practical barriers that have slowed adoption, with a focus on openness and usability.

At the hardware level, Embodied Tien Kung 3.0 is built for mobility, strength, and stability. It is equipped with high-torque integrated joints that provide strong limb force for high-load applications. The company says it is the first full-size humanoid robot to achieve whole-body, high-dynamic motion control integrated with tactile interaction. In practice, this means the robot is designed to maintain balance and execute dynamic movements even in uneven or cluttered environments. It can clear one-meter obstacles, perform consecutive high-dynamic maneuvers, and carry out actions such as kneeling, bending, and turning with coordinated whole-body control.

Precision is also a focus. Through multi-degree-of-freedom limb coordination and calibrated joint linkage, the system is designed to achieve millimeter-level operational accuracy. This level of control is intended to support industrial-grade tasks that require consistent performance and minimal error across changing conditions.

But hardware is only part of the equation. The company pairs the robot with its proprietary Wise KaiWu general-purpose embodied AI platform. This system supports perception, reasoning, and real-time control through what the company describes as a coordinated “brain–cerebellum” architecture. It establishes a continuous perception–decision–execution loop, allowing the robot to operate with greater autonomy and reduced reliance on remote control.

For higher-level cognition, Wise KaiWu incorporates components such as a world model and vision-language models (VLM) to interpret visual scenes, understand language instructions, and break complex objectives into structured steps. For real-time execution, a vision-language-action (VLA) model and full autonomous navigation system manage obstacle avoidance and precise motion under variable conditions. The platform also supports multi-agent collaboration, enabling cross-platform compatibility, asynchronous task coordination, and centralized scheduling across multiple robots.

A central part of the platform is openness. The company states that the system is designed to address compatibility and adaptation challenges across both development and deployment layers. On the hardware side, Embodied Tien Kung 3.0 includes multiple expansion interfaces that support different end-effectors and tools, allowing faster adaptation to industrial manufacturing, specialized operations, and commercial service scenarios. On the software side, the Wise KaiWu ecosystem provides documentation, toolchains, and a low-code development environment. It supports widely adopted communication standards, including ROS2, MQTT, and TCP/IP, enabling partners to customize applications without rebuilding core systems.

The company also highlights its open-source approach. X-Humanoid has open-sourced key components from the Embodied Tien Kung and Wise KaiWu platforms, including the robot body architecture, motion control framework, world model, embodied VLM and cross-ontology VLA models, training toolchains, the RoboMIND dataset, and the ArtVIP simulation asset library. By opening access to these elements, the company aims to reduce development costs, lower technical barriers, and encourage broader participation from researchers, universities, and enterprises.

Embodied Tien Kung 3.0 enters a market where technical progress is visible but large-scale adoption remains uneven. The gap is not only about movement or strength. It is about integration, interoperability, and the ability to operate reliably and autonomously in everyday industrial and commercial settings. If platforms can reduce fragmentation and simplify deployment, humanoid robots may move beyond demonstrations and into sustained commercial use.

In that sense, the significance of Embodied Tien Kung 3.0 lies less in isolated technical claims and more in how its high-dynamic hardware, embodied AI system, open interfaces, and collaborative architecture are structured to work together. Whether that integrated approach can close the deployment gap will shape how quickly humanoid robotics becomes part of real-world operations.

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Artificial Intelligence

How ChainGPT and Secret Network Bring Private, Verifiable AI Coding On-Chain

A step forward that could influence how smart contracts are designed and verified.

Updated

January 8, 2026 6:32 PM

ChainGPT's robot mascot. IMAGE: CHAINGPT

A new collaboration between ChainGPT, an AI company specialising in blockchain development tools and Secret Network, a privacy-focused blockchain platform, is redefining how developers can safely build smart contracts with artificial intelligence. Together, they’ve achieved a major industry first: an AI model trained exclusively to write and audit Solidity code is now running inside a Trusted Execution Environment (TEE). For the blockchain ecosystem, this marks a turning point in how AI, privacy and on-chain development can work together.

For years, smart-contract developers have faced a trade-off. AI assistants could speed up coding and security reviews, but only if developers uploaded their most sensitive source code to external servers. That meant exposing intellectual property, confidential logic and even potential vulnerabilities. In an industry where trust is everything, this risk held many teams back from using AI at all.

ChainGPT’s Solidity-LLM aims to solve that problem. It is a specialised large language model trained on over 650,000 curated Solidity contracts, giving it a deep understanding of how real smart contracts are structured, optimised and secured. And now, by running inside SecretVM, the Confidential Virtual Machine that powers Secret Network’s encrypted compute layer, the model can assist developers without ever revealing their code to outside parties.

“Confidential computing is no longer an abstract concept,” said Luke Bowman, COO of the Secret Network Foundation. “We've shown that you can run a complex AI model, purpose-built for Solidity, inside a fully encrypted environment and that every inference can be verified on-chain. This is a real milestone for both privacy and decentralised infrastructure”.

SecretVM makes this workflow possible by using hardware-backed encryption to protect all data while computations take place. Developers don’t interact with the underlying hardware or cryptography. Instead, they simply work inside a private, sealed environment where their code stays invisible to everyone except them—even node operators. For the first time, developers can generate, test and analyse smart contracts with AI while keeping every detail confidential.

This shift opens new possibilities for the broader blockchain community. Developers gain a private coding partner that can streamline contract logic or catch vulnerabilities without risking leaks. Auditors can rely on AI-assisted analysis while keeping sensitive audit material protected. Enterprises working in finance, healthcare or governance finally have a path to adopt AI-driven blockchain automation without raising compliance concerns. Even decentralised organisations can run smart-contract agents that make decisions privately, without exposing internal logic on a public chain.

The system also supports secure model training and fine-tuning on encrypted datasets. This enables collaborative AI development without forcing anyone to share raw data—a meaningful step toward decentralised and privacy-preserving AI at scale.

By combining specialised AI with confidential computing, ChainGPT and Secret Network are shifting the trust model of on-chain development. Instead of relying on centralised cloud AI services, developers now have a verifiable, encrypted environment where they keep full control of their code, their data and their workflow. It’s a practical solution to one of blockchain’s biggest challenges: using powerful AI tools without sacrificing privacy.

As the technology evolves, the roadmap includes confidential model fine-tuning, multi-agent AI systems and cross-chain use cases. But the core advancement is already clear: developers now have a way to use AI for smart contract development that is fast, private and verifiable—without compromising the security standards that decentralised systems rely on.