4 papers indexed
Peng Ren, Haoyang Ge, Jiang Zhao, Cong Huang, Yukun Shi, Pei Chi, et al.
Vision-language-action policies are a promising foundation for general robot control, but long-horizon humanoid loco-manipulation requires the robot to treat task objects as persistent physical entities across movement, contact, occlusion, and recovery. We study this problem as o…
Changti Wu, Bin Yu, Zhaolong Shen, Shijie Lian, Xiaopeng Lin, Cong Huang, et al.
Vision-Language-Action (VLA) models are typically trained by imitation learning on large-scale robot demonstration datasets, but more data does not necessarily yield better policies due to redundancy, noise, and uneven coverage. Existing data selection methods often assess demons…
Yue Han, Chong Li, Zhening Liu, Cong Huang, Fang Deng, Yong Liu, et al.
Recent 3D generative models can synthesize high-quality geometry but often struggle to reproduce intricate textures from reference images, largely due to the scarcity of large-scale 3D training data with rich surface appearance. In contrast, visual generative models are trained o…
Xiaopeng Lin, Ruoqi Yang, Shijie Lian, Zhaolong Shen, Bin Yu, Changti Wu, et al.
Vision-language-action (VLA) models across robot embodiments require high-quality observation--action supervision to learn deployable action distributions, yet scaling such robot data remains difficult, especially for high-DoF humanoids. Teleoperation provides controller-aligned…