works on

From the 1 of 15 linked papers with an AI index.

most citedData Scaling Laws in Imitation Learning for Robotic Manipulation

2 citations · 2 across the 4 of their papers we have counts for

collaborators

15 papers

cs.RO2026

When Does Legacy Data Start to Help? Emergent Transfer in Cross-Configuration Robot Learning

Tao Wang, Hudson Hou, Yingdong Hu +7

The paper investigates when demonstration data collected on an older robot configuration becomes useful for training a newer robot, revealing a three‑phase pattern where legacy dat…

cs.RO2026

Data Scaling Laws in Imitation Learning for Robotic Manipulation

Fanqi Lin, Yingdong Hu, Pingyue Sheng +3

Data scaling has revolutionized fields like natural language processing and computer vision, providing models with remarkable generalization capabilities. In this paper, we investi…

cs.RO2026

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…

cs.RO2026

FTP-1: A Generalist Foundation Tactile Policy Across Tactile Sensors for Contact-Rich Manipulation

Chengbo Yuan, Zicheng Zhang, Mingjie Zhou +14

Despite the success of vision-based generalist robotic policies, existing tactile-based policies remain tied to fixed embodiments and sensor setups. This is because tactile signals…

cs.RO2026

A Practical Recipe Towards Improving Sim-and-Real Correlation for VLA Evaluation

Shuo Wang, Hanyuan Xu, Yingdong Hu +2

Simulation has become an essential tool for evaluating and improving vision-language-action (VLA) policies, offering scalable, reproducible, and controllable alternatives to costly…

cs.GR2026

On the Controllability-Fidelity Frontier in Diffusion Editing

Yi Hu, Leying Yi, Emily Davis +1

Diffusion-based generative models enable powerful image editing capabilities, but achieving precise control while maintaining fidelity and safety remains challenging. We present a…