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LG-backed robotics startup RLWRLD releases AI model

RLWRLD, a robotics AI startup unveiled RLDX-1 as a foundation model for five-finger robotic hands aimed at industrial tasks.

The company also released its weights, code, and technical documents on GitHub and Hugging Face.

The company said RLDX-1 combines vision and language with robotic motion control.

The company also said it outperformed rival robotics model from Nvidia and Physical Intelligence across several benchmarks.

RLWRLD said it is working with more than 10 Korean and Japanese enterprises on robotics projects.

🔗 Source: RLWRLD

🧠 Food for thought

Implications, context, and why it matters.

US$41 million in seed funding and an industrial-first plan

  • RLWRLD raised about US$41 million across Seed 1 and Seed 2, said RLWRLD 1.
  • Jung-hee Ryu, RLWRLD founder, is a serial entrepreneur. His earlier company Olaworks, a Korean computer vision startup, was acquired by Intel in 2012. RLWRLD said it was Intel’s first acquisition of a Korean startup 2.
  • The company started in 2024 with a plan built on the manufacturing strength of Korea and Japan. It chose robotics foundation models over the crowded large language model, or LLM, market 3.
  • Seed investors include LG Electronics, CJ Logistics, a supply chain and delivery company, and Mitsui Chemicals, a Japanese chemicals manufacturer. RLWRLD said those ties give it real operating sites and business relationships that can support data collection plus deployments 1.

RLWRLD seeks a data edge from industrial deployments

  • The open-source release signals a bid to become the base software layer for many types of robots.
  • RLWRLD said it trains foundation models inside live industrial operations through its investor network. The company said this creates a proprietary real-world data edge over models built mostly in labs 1.
  • It is also building partnerships with robot makers including Rainbow Robotics, a South Korean robotics company, said RLWRLD 2.
  • This method pushes against robotics work done in isolation. It could move physical AI closer to regions with deep manufacturing expertise 3.

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