YZi Labs Leads $52 Million Funding Round, AI Industrial Robot RoboForce Accelerates Commercialization

Silicon Valley AI Robotics Company RoboForce Inc. announced on March 17 that it has completed $52 million in funding led by venture capital firm YZi Labs. This round attracted participation from Yahoo co-founder and former CEO Jerry Yang, Nobel laureate Myron Scholes, and others, bringing total funding to $67 million.

Cross-Industry Star Investment Team: Technology, Academic Finance, and Crypto Venture Capital Converge

The composition of investors in this round reflects the AI robotics sector’s appeal to diverse capital:

YZi Labs (Lead): A venture capital firm under the Binance ecosystem, focusing on Web3 and technological innovation. Ella Zhang joined the board.

Jerry Yang: Co-founder and former CEO of Yahoo, with over 20 years of experience in tech investments.

Myron Scholes: Nobel Prize in Economics laureate, known for the Black-Scholes options pricing model.

Gary Rieschel: Founding partner of Qiming Venture Partners, specializing in early-stage tech investments in China and the U.S.

Carnegie Mellon University: A top global research institution in robotics and engineering.

The funding will be used to advance foundational robot models, expand TITAN robot production capacity, and promote commercialization.

TITAN Robot: 11,000 Pre-Orders and Deep NVIDIA Integration

RoboForce’s flagship product, the TITAN robot, is designed for high-intensity environments such as solar power plant construction, data center maintenance, mining operations, dock handling, and warehouse logistics. Hardware specifications include millimeter-level precision and a 40 kg payload capacity (dual-arm configuration). As of the funding announcement, TITAN has accumulated over 11,000 pre-orders, demonstrating industrial clients’ strong interest in automation solutions.

In terms of technological ecosystem, RoboForce has established a deep partnership with NVIDIA. TITAN uses Jetson Thor edge processors for onboard AI inference and leverages NVIDIA’s Isaac simulation and training environment to model and test robot movements before deployment, significantly reducing trial-and-error costs during actual deployment.

Physical AI Stack: Data-Driven Continuous Learning Architecture

Founded in 2023, RoboForce employs a “Physical AI Stack” framework that integrates robot hardware, machine learning models, and simulation environments. Its core technology is the robot foundation model, trained using real-world telemetry data collected from deployed robots and synthetic data generated in simulation environments. This creates a closed-loop training system aimed at improving motion control, object handling, and task sequencing accuracy.

CEO Ma Lei stated: “Robotic labor is crucial for monotonous, dirty, and dangerous work. The core issue is human availability, cost, and safety. Our mission is to enable humans to focus on safer, more valuable work while robots undertake the most challenging industrial tasks.”

Frequently Asked Questions

Q: What is YZi Labs, and why are they leading investment in industrial AI robots?
A: YZi Labs is a venture capital firm under the Binance ecosystem, formerly Binance Labs, focusing on Web3 and cutting-edge technology innovation. Leading RoboForce’s funding indicates their expanding interest into physical AI and industrial automation, reflecting ongoing attention from crypto tech investors toward tangible AI sectors.

Q: What industries are RoboForce’s TITAN robots mainly used in?
A: TITAN robots are suitable for high-intensity industrial environments such as solar power plants, data centers, mining, docks, manufacturing plants, and logistics warehouses. They aim to replace manual labor in repetitive, hazardous, and physically demanding tasks, with over 11,000 pre-orders accumulated.

Q: Why is RoboForce’s partnership with NVIDIA important for its technological development?
A: RoboForce uses NVIDIA Jetson Thor for onboard AI inference and employs Isaac simulation environments for pre-deployment testing. This allows the foundational robot models to iterate and improve with richer simulated data, reducing deployment risks and enhancing reliability in industrial scenarios.