YZi Labs mène un tour de financement de 52 millions de dollars, le robot industriel IA RoboForce accélère sa commercialisation

Silicon Valley AI Robotics Company RoboForce Inc. announced on March 17th that it has completed a $52 million funding round 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-sector Star Investment Lineup: Technology, Academic Finance, and Crypto Venture Capital

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 Binance ecosystem, focusing on Web3 and technological innovation. Ella Zhang joined the board.

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

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

Gary Rieschel: Founding partner of Qiming Venture Partners, with deep early-stage tech investments in China and the US.

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

The funds 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 plants, data centers, mining operations, shipyards, and warehousing 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, indicating strong industrial customer demand for 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 employs NVIDIA’s Isaac simulation and training environment to model and test robot movements before deployment, significantly reducing trial-and-error costs in field deployment.

Physical AI Stack: Data-Driven Continuous Learning Architecture

Founded in 2023, RoboForce adopts a “Physical AI Stack” framework integrating robot hardware, machine learning models, and simulation environments. Its core technology is based on foundational robot models trained with 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 labor availability, cost, and safety. Our mission is to enable humans to engage in safer, more valuable work while robots undertake the most challenging industrial tasks.”

Frequently Asked Questions

Q: What is YZi Labs, and why is it leading the investment in industrial AI robots?
A: YZi Labs is a venture capital firm under Binance ecosystem, formerly Binance Labs, focusing on Web3 and frontier technology innovation. Leading RoboForce indicates its investment scope is extending into physical AI and industrial automation, reflecting ongoing interest from crypto tech VC circles in tangible AI sectors.

Q: What industries does RoboForce’s TITAN robot mainly serve?
A: TITAN robots are suitable for high-intensity industrial environments such as solar power plants, data centers, mining, shipyards, manufacturing plants, and warehousing logistics. They aim to replace repetitive, hazardous, and physically demanding manual 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 environment for pre-deployment testing, enabling iterative improvements of robot foundational models supported by richer simulated data, reducing deployment risks, and enhancing reliability in industrial scenarios.