1 citations · 1 across the 8 of their papers we have counts for
10 papers
T(R,O) Grasp++: Cross-Embodiment Dexterous Grasping in Cluttered Environments at Scale
Xin Fei, Zhixuan Xu, Huaicong Fang +5
Dexterous grasping in cluttered scenes remains a central challenge in robotic manipulation, as it requires high-dimensional hand coordination under complex object-environment const…
DexMimic: A Generalist Dexterous Hand-Object Tracking Policy via Value-Guided Reset Allocation
Zhexi Luo, Zhixuan Xu, Rui Yang +3
Achieving human-like dexterous manipulation remains a central challenge in robotics. Learning from human hand-object interaction (HOI) datasets offers a promising pathway to divers…
FingerEye: Learning Dexterous Manipulation with Continuous Vision-Tactile Sensing
Zhixuan Xu, Yichen Li, Xuanye Wu +2
Dexterous robotic manipulation requires perception that remains informative from pre-contact approach to contact initiation and post-contact control. We introduce FingerEye, a sens…
ContactExplorer: Contact Coverage-Guided Exploration for General-Purpose Dexterous Manipulation
Zixuan Liu, Ruoyi Qiao, Chenrui Tie +5
Reinforcement learning explores effectively in domains such as Atari games, navigation, and locomotion, where novelty over states or dynamics is a sufficient signal. In contrast, d…
ManiLong-Shot: Interaction-Aware One-Shot Imitation Learning for Long-Horizon Manipulation
Zixuan Chen, Chongkai Gao, Lin Shao +3
One-shot imitation learning (OSIL) offers a promising way to teach robots new skills without large-scale data collection. However, current OSIL methods are primarily limited to sho…
T(R,O) Grasp: Efficient Graph Diffusion of Robot-Object Spatial Transformation for Cross-Embodiment Dexterous Grasping
Xin Fei, Zhixuan Xu, Huaicong Fang +2
Dexterous grasping remains a central challenge in robotics due to the complexity of its high-dimensional state and action space. We introduce T(R,O) Grasp, a diffusion-based framew…
VLA-OS: Structuring and Dissecting Planning Representations and Paradigms in Vision-Language-Action Models
Chongkai Gao, Zixuan Liu, Zhenghao Chi +8
Recent studies on Vision-Language-Action (VLA) models have shifted from the end-to-end action-generation paradigm toward a pipeline involving task planning followed by action gener…
DexSinGrasp: Learning a Unified Policy for Dexterous Object Singulation and Grasping in Densely Cluttered Environments
Lixin Xu, Zixuan Liu, Zhewei Gui +6
Grasping objects in cluttered environments remains a fundamental yet challenging problem in robotic manipulation. While prior works have explored learning-based synergies between p…
TelePreview: A User-Friendly Teleoperation System with Virtual Arm Assistance for Enhanced Effectiveness
Jingxiang Guo, Jiayu Luo, Zhenyu Wei +5
Teleoperation provides an effective way to collect robot data, which is crucial for learning from demonstrations. In this field, teleoperation faces several key challenges: user-fr…
FLIP: Flow-Centric Generative Planning as General-Purpose Manipulation World Model
Chongkai Gao, Haozhuo Zhang, Zhixuan Xu +2
We aim to develop a model-based planning framework for world models that can be scaled with increasing model and data budgets for general-purpose manipulation tasks with only langu…