Event
RynnUnify Releases 16B Unified Embodied Model
2 reports · 2 sources
Minghao Zhu and other authors released RynnUnify, a 16B unified embodied model that combines embodied understanding with long-horizon visual planning through interleaved video-language reasoning, covering spatial reasoning, physical prediction, and grounding. The authors also developed an agentic extension based on reinforcement learning to selectively leverage video prediction for reasoning about unobserved states and interaction outcomes. The authors additionally developed a world-action extension that applies understanding and prediction capabilities to robot control. According to the authors, this extension achieved a 51.5% success rate on 20 selected RoboCasa365 composite tasks and an average success rate of 46.7% on three real-robot tasks.
Report timeline
- RynnUnify: 16B unified embodied model combines physical understanding with long-horizon visual planning
The authors propose RynnUnify, a 16B unified embodied model that combines embodied understanding with long-horizon visual planning through video-language interleaved reasoning. The model covers spatial reasoning, physical prediction, and grounding capabilities, linking physical world understanding with visual anticipation across subtasks. The post also provides links to the code and paper.
Reddit r/robotics ·
- RynnUnify Releases 16B Unified Embodied Model Combining Visual Planning and Robot Control
RynnUnify is a 16B unified embodied model that combines embodied understanding with long-horizon visual planning through interleaved video-language reasoning, covering spatial reasoning, physical prediction, and grounding. The authors also developed an agentic extension based on reinforcement learning to selectively use video prediction to reason about unobserved states and interaction outcomes, and a world-action extension that translates understanding and prediction capabilities into robot control.
arXiv cs.RO ·