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[, This work was supported by NTUitive Gap Fund (NGF-2025-17006) and National Research Foundation, Singapore, under its Medium-Sized Center for Advanced Robotics Technology Innovation. Zhongyuang Liu is with CertaintyX. Min He, Shaonan Yu, Xinhang Xu, Jianping Li and Lihua Xie are with the School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798. Jianfei Yang is with the School of Mechanical and Aerospace Engineering, and jointly with the School of Electrical and Electronic Engineering at Nanyang Technological University, Singapore. Muqing Cao is with the Robotics Institute, Carnegie Mellon University, Pittsburgh, PA 15213 USA. (Zhongyuang Liu and Min He contributed equally to this work. Jianping Li is the corresponding author. E-mail: jianping.li@ntu.edu.sg)
CMU Machine Learning1
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RynnBrain leapfrogs existing embodied foundation models, offering a unified, open-source spatiotemporal model that excels at physically grounded reasoning and planning across a wide range of benchmarks.
