2 citations · 4 across the 11 of their papers we have counts for
14 papers
Function beyond Form: Functional Correspondence for Cross-Embodiment Dexterous Grasp Generation
Bolin Zou, Wenlong Dong, Mu Ai +4
Cross-embodiment dexterous grasp generation remains challenging because robotic hands differ substantially in geometry, topology, and kinematics. Existing approaches often lack exp…
RoboICL: Embodied In-Context Learning with GPT-6 Astra
Fangcheng Liu, Yeqing Shen, Anda Cheng +7
General-purpose vision-language models offer a promising way to zero-shot robot control: \gptastra{} excels at open-ended and language- or image-conditioned manipulation but remain…
Imperfection for Precision: Upcycling Imperfect Data for High-Precision Robotic Manipulation
Hao Wei, Yang Liu, Chao Tang +7
Training vision-language-action (VLA) models for high-precision manipulation typically requires task-specific, high-quality data (e.g., teleoperation), which is slow and expensive…
What Matters in Designing World Action Models: An Empirical Study
Chao Tang, Haoqing Wang, Zilang Cen +9
World Action Models (WAMs) have emerged as a promising paradigm for generalizable robot control. Despite the growing number of WAM systems, existing works often introduce unified s…
PSR: Predictive Sensorimotor Representation Learning for Contact-Rich Manipulation
Shengbao Li, Peng Xu, Chao Tang +8
Contact-rich manipulation requires policies to generate precise actions by reasoning over contact forces, robot configurations, and interaction histories beyond visual observations…
Grasp as You Dream: Imitating Functional Grasping from Generated Human Demonstrations
Chao Tang, Jiacheng Xu, Haofei Lu +4
Building generalist robots capable of performing functional grasping in everyday, open-world environments remains a significant challenge due to the vast diversity of objects and t…
Easy-IIL: Reducing Human Operational Burden in Interactive Imitation Learning via an Assistant Expert
Chengjie Zhang, Chao Tang, Wenlong Dong +3
Interactive Imitation Learning (IIL) typically relies on extensive human involvement for both offline demonstration and online interaction. Prior work primarily focuses on reducing…
Dexterous Manipulation through Imitation Learning: A Survey
Shan An, Ziyu Meng, Chao Tang +9
Dexterous manipulation, which refers to the ability of a robotic hand or multi-fingered end-effector to skillfully control, reorient, and manipulate objects through precise, coordi…
FlowPlan: Zero-Shot Task Planning with LLM Flow Engineering for Robotic Instruction Following
Zijun Lin, Chao Tang, Hanjing Ye +1
Robotic instruction following tasks require seamless integration of visual perception, task planning, target localization, and motion execution. However, existing task planning met…
HGDiffuser: Efficient Task-Oriented Grasp Generation via Human-Guided Grasp Diffusion Models
Dehao Huang, Wenlong Dong, Chao Tang +1
Task-oriented grasping (TOG) is essential for robots to perform manipulation tasks, requiring grasps that are both stable and compliant with task-specific constraints. Humans natur…
FUNCTO: Function-Centric One-Shot Imitation Learning for Tool Manipulation
Chao Tang, Anxing Xiao, Yuhong Deng +5
Learning tool use from a single human demonstration video offers a highly intuitive and efficient approach to robot teaching. While humans can effortlessly generalize a demonstrate…
RTAGrasp: Learning Task-Oriented Grasping from Human Videos via Retrieval, Transfer, and Alignment
Wenlong Dong, Dehao Huang, Jiangshan Liu +2
Task-oriented grasping (TOG) is crucial for robots to accomplish manipulation tasks, requiring the determination of TOG positions and directions. Existing methods either rely on co…
FoundationGrasp: Generalizable Task-Oriented Grasping with Foundation Models
Chao Tang, Dehao Huang, Wenlong Dong +2
Task-oriented grasping (TOG), which refers to synthesizing grasps on an object that are configurationally compatible with the downstream manipulation task, is the first milestone t…
Commonsense Scene Graph-based Target Localization for Object Search
Wenqi Ge, Chao Tang, Hong Zhang
Object search is a fundamental skill for household robots, yet the core problem lies in the robot's ability to locate the target object accurately. The dynamic nature of household…