5 citations · 10 across the 8 of their papers we have counts for
8 papers
OpenHLM: An Empirical Recipe for Whole-Body Humanoid Loco-Manipulation
Yingdong Hu, Haodong Zhu, Boyuan Zheng +6
Whole-body humanoid loco-manipulation requires coordinating the robot's entire kinematic chain. However, most existing systems typically decouple the upper and lower bodies into se…
Humanoid Manipulation Interface: Humanoid Whole-Body Manipulation from Robot-Free Demonstrations
Ruiqian Nai, Boyuan Zheng, Junming Zhao +8
Current approaches for humanoid whole-body manipulation, primarily relying on teleoperation or visual sim-to-real reinforcement learning, are hindered by hardware logistics and com…
OneTwoVLA: A Unified Vision-Language-Action Model with Adaptive Reasoning
Fanqi Lin, Ruiqian Nai, Yingdong Hu +3
General-purpose robots capable of performing diverse tasks require synergistic reasoning and acting capabilities. However, recent dual-system approaches, which separate high-level…
HuB: Learning Extreme Humanoid Balance
Tong Zhang, Boyuan Zheng, Ruiqian Nai +8
The human body demonstrates exceptional motor capabilities-such as standing steadily on one foot or performing a high kick with the leg raised over 1.5 meters-both requiring precis…
Fine-Tuning Hard-to-Simulate Objectives for Quadruped Locomotion: A Case Study on Total Power Saving
Ruiqian Nai, Jiacheng You, Liu Cao +4
Legged locomotion is not just about mobility; it also encompasses crucial objectives such as energy efficiency, safety, and user experience, which are vital for real-world applicat…
Revisiting Disentanglement in Downstream Tasks: A Study on Its Necessity for Abstract Visual Reasoning
Ruiqian Nai, Zixin Wen, Ji Li +2
In representation learning, a disentangled representation is highly desirable as it encodes generative factors of data in a separable and compact pattern. Researchers have advocate…
An Empirical Study on Disentanglement of Negative-free Contrastive Learning
Jinkun Cao, Ruiqian Nai, Qing Yang +2
Negative-free contrastive learning methods have attracted a lot of attention with simplicity and impressive performances for large-scale pretraining. However, its disentanglement p…
Real Additive Margin Softmax for Speaker Verification
Lantian Li, Ruiqian Nai, Dong Wang
The additive margin softmax (AM-Softmax) loss has delivered remarkable performance in speaker verification. A supposed behavior of AM-Softmax is that it can shrink within-class var…