1 citations · 1 across the 5 of their papers we have counts for
5 papers
RynnBrain 1.1: Towards More Capable and Generalizable Embodied Foundation Model
Kehan Li, Bohan Hou, Minghao Zhu +28
We present RynnBrain 1.1, a family of embodied foundation models spanning 2B, 9B, and 122B-A10B scales. Trained with a unified spatio-temporal and physically grounded framework, Ry…
RynnBrain: Open Embodied Foundation Models
Ronghao Dang, Jiayan Guo, Bohan Hou +23
Despite rapid progress in multimodal foundation models, embodied intelligence community still lacks a unified, physically grounded foundation model that integrates perception, reas…
High-Fidelity Simulated Data Generation for Real-World Zero-Shot Robotic Manipulation Learning with Gaussian Splatting
Haoyu Zhao, Cheng Zeng, Linghao Zhuang +11
The scalability of robotic learning is fundamentally bottlenecked by the significant cost and labor of real-world data collection. While simulated data offers a scalable alternativ…
RynnVLA-001: Using Human Demonstrations to Improve Robot Manipulation
Yuming Jiang, Siteng Huang, Shengke Xue +10
This paper presents RynnVLA-001, a vision-language-action(VLA) model built upon large-scale video generative pretraining from human demonstrations. We propose a novel two-stage pre…
AesopAgent: Agent-driven Evolutionary System on Story-to-Video Production
Jiuniu Wang, Zehua Du, Yuyuan Zhao +9
The Agent and AIGC (Artificial Intelligence Generated Content) technologies have recently made significant progress. We propose AesopAgent, an Agent-driven Evolutionary System on S…