Kunlun Wanwei spins out robotics unit and trains Riemann-1.0 on 232,000 hours of embodied data
创业邦 · wechat · 2026-07-21
From world models to robots
Kunlun Wanwei’s internal team, now operating as Riemann Dynamics, has shifted from game-world generation and video models toward embodied AI and robotics.
What they built
- They introduced Riemann-1.0, a world action model aimed at long-horizon tasks, cross-scenario generalization, and cross-embodiment transfer.
- The team argues robots still lack a unified API like ChatGPT, and wants to create a cross-robot interface so different hardware can reuse the same model.
- They say the model jointly learns robot actions and the environment changes those actions cause, allowing the robot to observe results, update its state, and revise plans.
Data and scaling
- The model is trained on 232,000 hours of multimodal embodied interaction data.
- That includes 200,000+ hours of first-person human video, 12,000 hours of high-precision demonstration data, and 20,000 hours of real-robot plus simulation trajectories.
- The team says robotics still lacks an internet-scale data source comparable to CommonCrawl or ImageNet, so it is leaning on crowdsourced first-person capture.
Product and business direction
- The company is testing domestic commercialization through a robot API for hardware makers who lack strong model teams.
- Abroad, it is exploring paid demand in Japan, especially for hotels, elder care, airports, and retail.
- The long-term bet is that robots could move from selling hardware to selling services, with a new token-like economy around robotic capabilities.
Related event: Kunlun Wanwei Spins Off Riemann Dynamics, Launches World Action Model(2 posts)→
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