6 citations · 11 across the 7 of their papers we have counts for
7 papers · 1 filter
EvoHIL: Self-Evolving Reward and Flow-Matched Policy Optimization for Robust Human-in-the-Loop Reinforcement Learning
Shuoqin Zhang, Tongtong Cheng, Xiru Gao +7
Human-in-the-loop reinforcement learning (HIL-RL) enables robots to learn contact-rich manipulation from limited real-world interaction, but deployment exposes three coupled limita…
HSC-VLA: Hierarchical Scene-Clearing for Robust Bimanual Manipulation in Dense Clutter
Zhen Liu, Xinyu Ning, Zhe Hu +3
Modern Vision--Language--Action models often suffer from critical instruction-following failures in high-density manipulation environments, where task-irrelevant visual clutter dil…
Safe Navigation with Human Instructions in Complex Scenes
Zhe Hu, Jia Pan, Tingxiang Fan +2
In this paper, we present a robotic navigation algorithm with natural language interfaces, which enables a robot to safely walk through a changing environment with moving persons b…
Learning-based Feedback Controller for Deformable Object Manipulation
Biao Jia, Zhe Hu, Zherong Pan +2
In this paper, we present a general learning-based framework to automatically visual-servo control the position and shape of a deformable object with unknown deformation parameters…
Cloth Manipulation Using Random-Forest-Based Imitation Learning
Biao Jia, Zherong Pan, Zhe Hu +2
We present a novel approach for robust manipulation of high-DOF deformable objects such as cloth. Our approach uses a random forest-based controller that maps the observed visual f…
3D Deformable Object Manipulation using Fast Online Gaussian Process Regression
Zhe Hu, Peigen Sun, Jia Pan
In this paper, we present a general approach to automatically visual-servo control the position and shape of a deformable object whose deformation parameters are unknown. The servo…
Manipulating Highly Deformable Materials Using a Visual Feedback Dictionary
Biao Jia, Zhe Hu, Jia Pan +1
The complex physical properties of highly deformable materials such as clothes pose significant challenges fanipulation systems. We present a novel visual feedback dictionary-based…