RLWRLD, a Seoul-based startup, is drawing attention with its drive to build robust robot foundation models for industry. As robots grow in complexity and autonomy, training them in real-world conditions, rather than controlled lab settings, becomes essential. RLWRLD collaborates with WIRobotics and various strategic partners to expose its systems—like the ALLEX robot—to unpredictable industrial environments. Their focus on direct, on-site data collection sets them apart in the race for physical AI, aiming to create models with genuine practical utility. Recent investments reflect confidence in their approach to physical AI’s challenges and potential.
Back in April 2025, RLWRLD closed its initial seed funding of $14.8 million, signaling rising investor interest in robotics with practical AI for factories and warehouses. At that time, the company emphasized foundational research and early-stage testing. Recently, RLWRLD shifted focus towards scaling partnerships with industry leaders, validating models in real operations, and forging joint ventures. The firm’s trajectory shows an evolution from concept to partnerships involving operational trials, distinguished by increased buy-in from established corporate investors like CJ Logistics and Lotte Ventures compared to earlier smaller-scale engagements.
How Does RLWRLD Train Robots for Real Workplaces?
Unlike many competitors that rely on simulated or laboratory data, RLWRLD captures high-precision, multimodal data directly within functioning factories, warehouses, and service settings. This exposure to challenging, variable conditions—such as inconsistent lighting, crowded workspaces, and human interaction—aims to prepare models for the complexities of real industrial operations. Training based on unpredictable environments can help the company’s solutions adapt robustly to diverse, imperfect situations that are rarely represented in simulations.
What Do RLWRLD’s Strategic Collaborations Involve?
Partnering with firms like CJ Logistics and Lotte, RLWRLD progresses from initial agreements to hands-on validation of AI systems in logistics and retail operations across Japan and South Korea. By leveraging operational data from these environments, the partnerships target practical evaluations and deployments of robot foundation models. A representative from CJ Logistics observed,
The future of logistics competitiveness depends not on basic automation, but on AI systems that can understand and reason about complex physical environments.
Who Supports RLWRLD’s Recent Funding and Why?
To meet increased enterprise demand for validated, site-specific AI solutions, RLWRLD’s recent Seed 2 round raised an additional $26 million, bringing total seed funding to $42 million. Investors include both financial venture firms such as Headline Asia and Z Ventures, as well as strategic investors like Kakao Investment, Hanwha Asset Management, and CJ Logistics. Speaking about the approach, RLWRLD’s CEO Junghee Ryu stated,
Physical AI only matters if it works on real job sites. By testing our models in some of the toughest industrial environments, we’re building systems meant for real operations first—before they ever make sense elsewhere.
RLWRLD’s focus on using actual operational data marks a shift in robotics research priorities from lab-based to field-based AI model development. Companies aiming to automate or augment physical tasks face substantial hurdles due to unpredictable conditions in real-world workplaces, making adaptable AI vital. For investors, the emphasis on collaborative validation and partnerships addresses the need for robots that can navigate complexity, rather than rely on idealized, simulated scenarios. RLWRLD’s trajectory highlights a broader movement within robotics toward solutions that can integrate seamlessly with existing workflows, maintain reliability, and evolve with the workplace’s dynamic needs.
