1 citations · 2 across the 12 of their papers we have counts for
14 papers
PanoFuse: Panorama-Enhanced Vision-Language-Action Learning with Decoupled Semantic-Geometric Routing
Peng Xu, Haoran Lin, Wanjun Jia +4
Vision-Language-Action (VLA) policies have shown promising performance in language-conditioned robotic manipulation. However, most existing VLA systems rely on conventional perspec…
TONAV: Task-Oriented Navigation and Action-Velocity Chunk Learning for Articulated Object Quadrupedal Mobile Manipulation
Haoran Lin, Mingyu Yang, Pengfei Qi +6
Quadruped mobile manipulation requires two tightly coupled capabilities: reaching manipulation-ready configurations and maintaining stable contact throughout articulated-object int…
DexPIE: Stable Dexterous Policy Improvement from Real-World Experience
Ruizhe Liao, Wenrui Chen, Liangji Zeng +4
Dexterous manipulation presents substantial challenges for imitation learning due to its high-dimensional action space and complex contact-rich dynamics. Policies trained purely fr…
Symmetry-Aware 9D Pose Estimation with Sim(3)-Consistent Feature and Spherical Inception Convolution
Panfei Cheng, Hongshan Yu, Wenrui Chen +3
Object pose estimation is a fundamental problem for an agent system to perceive or manipulate objects in images or videos. However, current instance-level methods struggle with gen…
BLaDA: Bridging Language to Functional Dexterous Actions within 3DGS Fields
Fan Yang, Wenrui Chen, Guorun Yan +7
In unstructured environments, functional dexterous grasping calls for the tight integration of semantic understanding, precise 3D functional localization, and physically interpreta…
TRACER: Texture-Robust Affordance Chain-of-Thought for Deformable-Object Region Grounding
Wanjun Jia, Kang Li, Fan Yang +7
The central challenge in robotic manipulation of deformable objects lies in aligning high-level semantic instructions with physical interaction points under complex appearance and…
Learning Fine-Grained Correspondence with Cross-Perspective Perception for Open-Vocabulary 6D Object Pose Estimation
Yu Qin, Shimeng Fan, Fan Yang +5
Open-vocabulary 6D object pose estimation empowers robots to manipulate arbitrary unseen objects guided solely by natural language. However, a critical limitation of existing appro…
Design of an Adaptive Modular Anthropomorphic Dexterous Hand for Human-like Manipulation
Zelong Zhou, Wenrui Chen, Zeyun Hu +3
Biological synergies have emerged as a widely adopted paradigm for dexterous hand design, enabling human-like manipulation with a small number of actuators. Nonetheless, excessive…
UniFucGrasp: Human-Hand-Inspired Unified Functional Grasp Annotation Strategy and Dataset for Diverse Dexterous Hands
Haoran Lin, Wenrui Chen, Xianchi Chen +7
Dexterous grasp datasets are vital for embodied intelligence, but mostly emphasize grasp stability, ignoring functional grasps needed for tasks like opening bottle caps or holding…
Resource-Efficient Affordance Grounding with Complementary Depth and Semantic Prompts
Yizhou Huang, Fan Yang, Guoliang Zhu +6
Affordance refers to the functional properties that an agent perceives and utilizes from its environment, and is key perceptual information required for robots to perform actions.…
One-Shot Affordance Grounding of Deformable Objects in Egocentric Organizing Scenes
Wanjun Jia, Fan Yang, Mengfei Duan +6
Deformable object manipulation in robotics presents significant challenges due to uncertainties in component properties, diverse configurations, visual interference, and ambiguous…
Multi-Keypoint Affordance Representation for Functional Dexterous Grasping
Fan Yang, Dongsheng Luo, Wenrui Chen +5
Functional dexterous grasping requires precise hand-object interaction, going beyond simple gripping. Existing affordance-based methods primarily predict coarse interaction regions…
Exploiting Hybrid Policy in Reinforcement Learning for Interpretable Temporal Logic Manipulation
Hao Zhang, Hao Wang, Xiucai Huang +2
Reinforcement Learning (RL) based methods have been increasingly explored for robot learning. However, RL based methods often suffer from low sampling efficiency in the exploration…
Learning Granularity-Aware Affordances from Human-Object Interaction for Tool-Based Functional Dexterous Grasping
Fan Yang, Wenrui Chen, Kailun Yang +5
To enable robots to use tools, the initial step is teaching robots to employ dexterous gestures for touching specific areas precisely where tasks are performed. Affordance features…