4 citations · 7 across the 18 of their papers we have counts for
21 papers
OmniInteract: Towards Unified Whole-Body Interaction Control for Dual Humanoids
Yinghao Li, Tianyu Zhang, Zhuohao Li +4
Physical interaction is a key capability for humanoid collaboration, requiring coordinated whole-body motion and precise contact. However, learning diverse dual-humanoid interactio…
TrapVLA: Trapping Vision-Language-Action Models in Configured Failure Modes
Jun-Hui Liu, Kun-Yu Lin, Yi-Lin Wei +9
This work introduces Configured Failure Trapping, a novel backdoor attack task against Vision-Language-Action (VLA) models, which aims to activate attacks through stealthy textual…
DynamicManip: Enabling Dynamic Manipulation from a Single Static Demonstration
Haoran Liao, Pengyue Wang, Shuoyu Chen +10
Dynamic manipulation is a critical capability for robots operating in complex and dynamic environments, where robots must interact with objects that are moving or require rapid adj…
Humanoid Whole-Body Manipulation via Active Spatial Brain and Generalizable Action Cerebellum
Zhizhao Liang, Yi-Lin Wei, Xuhang Chen +6
In this paper, we explore spatial-aware humanoid whole-body manipulation task. Compared with tabletop settings, this task poses two key challenges: 1) Spatial understanding is chal…
BiDexGrasp: Coordinated Bimanual Dexterous Grasps across Object Geometries and Sizes
Mu Lin, Yi-Lin Wei, Jiaxuan Chen +7
Bimanual dexterous grasping is a fundamental and promising area in robotics, yet its progress is constrained by the lack of comprehensive datasets and powerful generation models. I…
DexGrasp-Zero: A Morphology-Aligned Policy for Zero-Shot Cross-Embodiment Dexterous Grasping
Yuliang Wu, Yanhan Lin, WengKit Lao +4
To meet the demands of increasingly diverse dexterous hand hardware, it is crucial to develop a policy that enables zero-shot cross-embodiment grasping without redundant re-learnin…
Learning Whole-Body Human-Humanoid Interaction from Human-Human Demonstrations
Wei-Jin Huang, Yue-Yi Zhang, Yi-Lin Wei +5
Enabling humanoid robots to physically interact with humans is a critical frontier, but progress is hindered by the scarcity of high-quality Human-Humanoid Interaction (HHoI) data.…
CycleManip: Enabling Cyclic Task Manipulation via Effective Historical Perception and Understanding
Yi-Lin Wei, Haoran Liao, Yuhao Lin +4
In this paper, we explore an important yet underexplored task in robot manipulation: cycle-based manipulation, where robots need to perform cyclic or repetitive actions with an exp…
ZeroDexGrasp: Zero-Shot Task-Oriented Dexterous Grasp Synthesis with Prompt-Based Multi-Stage Semantic Reasoning
Juntao Jian, Yi-Lin Wei, Chengjie Mou +5
Task-oriented dexterous grasping holds broad application prospects in robotic manipulation and human-object interaction. However, most existing methods still struggle to generalize…
OmniDexGrasp: Generalizable Dexterous Grasping via Foundation Model and Force Feedback
Yi-Lin Wei, Zhexi Luo, Yuhao Lin +4
Enabling robots to dexterously grasp and manipulate objects based on human commands is a promising direction in robotics. However, existing approaches are challenging to generalize…
TypeTele: Releasing Dexterity in Teleoperation by Dexterous Manipulation Types
Yuhao Lin, Yi-Lin Wei, Haoran Liao +6
Dexterous teleoperation plays a crucial role in robotic manipulation for real-world data collection and remote robot control. Previous dexterous teleoperation mostly relies on hand…
ChainHOI: Joint-based Kinematic Chain Modeling for Human-Object Interaction Generation
Ling-An Zeng, Guohong Huang, Yi-Lin Wei +4
We propose ChainHOI, a novel approach for text-driven human-object interaction (HOI) generation that explicitly models interactions at both the joint and kinetic chain levels. Unli…
iManip: Skill-Incremental Learning for Robotic Manipulation
Zexin Zheng, Jia-Feng Cai, Xiao-Ming Wu +3
The development of a generalist agent with adaptive multiple manipulation skills has been a long-standing goal in the robotics community. In this paper, we explore a crucial task,…
TacCap: A Wearable FBG-Based Tactile Sensor for Seamless Human-to-Robot Skill Transfer
Chengyi Xing, Hao Li, Yi-Lin Wei +6
Tactile sensing is essential for dexterous manipulation, yet large-scale human demonstration datasets lack tactile feedback, limiting their effectiveness in skill transfer to robot…
Rethinking Bimanual Robotic Manipulation: Learning with Decoupled Interaction Framework
Jian-Jian Jiang, Xiao-Ming Wu, Yi-Xiang He +4
Bimanual robotic manipulation is an emerging and critical topic in the robotics community. Previous works primarily rely on integrated control models that take the perceptions and…
AffordDexGrasp: Open-set Language-guided Dexterous Grasp with Generalizable-Instructive Affordance
Yi-Lin Wei, Mu Lin, Yuhao Lin +4
Language-guided robot dexterous generation enables robots to grasp and manipulate objects based on human commands. However, previous data-driven methods are hard to understand inte…
Real-to-Sim Grasp: Rethinking the Gap between Simulation and Real World in Grasp Detection
Jia-Feng Cai, Zibo Chen, Xiao-Ming Wu +3
For 6-DoF grasp detection, simulated data is expandable to train more powerful model, but it faces the challenge of the large gap between simulation and real world. Previous works…
An Economic Framework for 6-DoF Grasp Detection
Xiao-Ming Wu, Jia-Feng Cai, Jian-Jian Jiang +3
Robotic grasping in clutters is a fundamental task in robotic manipulation. In this work, we propose an economic framework for 6-DoF grasp detection, aiming to economize the resour…
Grasp as You Say: Language-guided Dexterous Grasp Generation
Yi-Lin Wei, Jian-Jian Jiang, Chengyi Xing +5
This paper explores a novel task "Dexterous Grasp as You Say" (DexGYS), enabling robots to perform dexterous grasping based on human commands expressed in natural language. However…
Dexterous Grasp Transformer
Guo-Hao Xu, Yi-Lin Wei, Dian Zheng +2
In this work, we propose a novel discriminative framework for dexterous grasp generation, named Dexterous Grasp TRansformer (DGTR), capable of predicting a diverse set of feasible…
Single-View Scene Point Cloud Human Grasp Generation
Yan-Kang Wang, Chengyi Xing, Yi-Lin Wei +2
In this work, we explore a novel task of generating human grasps based on single-view scene point clouds, which more accurately mirrors the typical real-world situation of observin…