6 citations · 15 across the 20 of their papers we have counts for
22 papers
FlatLab: A Unified Methodology Framework and Simulation-Based Benchmark for Robotic Manipulation of Flat Objects
Xingyu Zhu, Wenshuo Han, Zhouyu Wang +7
Robotic manipulation of flat objects is challenging due to the ungraspable configurations and strong variations in object geometry and material. Existing methods rely on heuristic…
Sparse Meets Dense: Correspondence Guided Robotic Manipulation with Rigid-Deformable Interactions
Ziyu Zhu, Yue Chen, Xirui Liang +5
Manipulation involving rigid-deformable interactions, such as hanging clothes or dressing humans, is common in daily life, making it essential for household robots. Compared to sin…
HeteroGenManip: Generalizable Manipulation For Heterogeneous Object Interactions
Zhenhao Shen, Zeming Yang, Yue Chen +5
Generalizable manipulation involving cross-type object interactions is a critical yet challenging capability in robotics. To reliably accomplish such tasks, robots must address two…
HiPolicy: Hierarchical Multi-Frequency Action Chunking for Policy Learning
Jiyao Zhang, Zimu Han, Junhan Wang +7
Robotic imitation learning faces a fundamental trade-off between modeling long-horizon dependencies and enabling fine-grained closed-loop control. Existing fixed-frequency action c…
GraspALL: Adaptive Structural Compensation from Illumination Variation for Robotic Garment Grasping in Any Low-Light Conditions
Haifeng Zhong, Wenshuo Han, Zhouyu Wang +10
Achieving accurate garment grasping under dynamically changing illumination is crucial for all-day operation of service robots.However, the reduced illumination in low-light scenes…
GarmentPile++: Affordance-Driven Cluttered Garments Retrieval with Vision-Language Reasoning
Mingleyang Li, Yuran Wang, Yue Chen +6
Garment manipulation has attracted increasing attention due to its critical role in home-assistant robotics. However, the majority of existing garment manipulation works assume an…
Learning Part-Aware Dense 3D Feature Field for Generalizable Articulated Object Manipulation
Yue Chen, Muqing Jiang, Kaifeng Zheng +5
Articulated object manipulation is essential for various real-world robotic tasks, yet generalizing across diverse objects remains a major challenge. A key to generalization lies i…
A3D: Adaptive Affordance Assembly with Dual-Arm Manipulation
Jiaqi Liang, Yue Chen, Qize Yu +4
Furniture assembly is a crucial yet challenging task for robots, requiring precise dual-arm coordination where one arm manipulates parts while the other provides collaborative supp…
BiAssemble: Learning Collaborative Affordance for Bimanual Geometric Assembly
Yan Shen, Ruihai Wu, Yubin Ke +6
Shape assembly, the process of combining parts into a complete whole, is a crucial robotic skill with broad real-world applications. Among various assembly tasks, geometric assembl…
DexGarmentLab: Dexterous Garment Manipulation Environment with Generalizable Policy
Yuran Wang, Ruihai Wu, Yue Chen +7
Garment manipulation is a critical challenge due to the diversity in garment categories, geometries, and deformations. Despite this, humans can effortlessly handle garments, thanks…
GarmentPile: Point-Level Visual Affordance Guided Retrieval and Adaptation for Cluttered Garments Manipulation
Ruihai Wu, Ziyu Zhu, Yuran Wang +3
Cluttered garments manipulation poses significant challenges due to the complex, deformable nature of garments and intricate garment relations. Unlike single-garment manipulation,…
GarmentLab: A Unified Simulation and Benchmark for Garment Manipulation
Haoran Lu, Ruihai Wu, Yitong Li +7
Manipulating garments and fabrics has long been a critical endeavor in the development of home-assistant robots. However, due to complex dynamics and topological structures, garmen…
ManipGPT: Is Affordance Segmentation by Large Vision Models Enough for Articulated Object Manipulation?
Taewhan Kim, Hojin Bae, Zeming Li +4
Visual actionable affordance has emerged as a transformative approach in robotics, focusing on perceiving interaction areas prior to manipulation. Traditional methods rely on pixel…
ET-SEED: Efficient Trajectory-Level SE(3) Equivariant Diffusion Policy
Chenrui Tie, Yue Chen, Ruihai Wu +4
Imitation learning, e.g., diffusion policy, has been proven effective in various robotic manipulation tasks. However, extensive demonstrations are required for policy robustness an…
EqvAfford: SE(3) Equivariance for Point-Level Affordance Learning
Yue Chen, Chenrui Tie, Ruihai Wu +1
Humans perceive and interact with the world with the awareness of equivariance, facilitating us in manipulating different objects in diverse poses. For robotic manipulation, such e…
Broadcasting Support Relations Recursively from Local Dynamics for Object Retrieval in Clutters
Yitong Li, Ruihai Wu, Haoran Lu +4
In our daily life, cluttered objects are everywhere, from scattered stationery and books cluttering the table to bowls and plates filling the kitchen sink. Retrieving a target obje…
UniGarmentManip: A Unified Framework for Category-Level Garment Manipulation via Dense Visual Correspondence
Ruihai Wu, Haoran Lu, Yiyan Wang +2
Garment manipulation (e.g., unfolding, folding and hanging clothes) is essential for future robots to accomplish home-assistant tasks, while highly challenging due to the diversity…
NaturalVLM: Leveraging Fine-grained Natural Language for Affordance-Guided Visual Manipulation
Ran Xu, Yan Shen, Xiaoqi Li +2
Enabling home-assistant robots to perceive and manipulate a diverse range of 3D objects based on human language instructions is a pivotal challenge. Prior research has predominantl…
Learning Part Motion of Articulated Objects Using Spatially Continuous Neural Implicit Representations
Yushi Du, Ruihai Wu, Yan Shen +1
Articulated objects (e.g., doors and drawers) exist everywhere in our life. Different from rigid objects, articulated objects have higher degrees of freedom and are rich in geometr…
Learning Environment-Aware Affordance for 3D Articulated Object Manipulation under Occlusions
Ruihai Wu, Kai Cheng, Yan Shen +3
Perceiving and manipulating 3D articulated objects in diverse environments is essential for home-assistant robots. Recent studies have shown that point-level affordance provides ac…
Where2Explore: Few-shot Affordance Learning for Unseen Novel Categories of Articulated Objects
Chuanruo Ning, Ruihai Wu, Haoran Lu +2
Articulated object manipulation is a fundamental yet challenging task in robotics. Due to significant geometric and semantic variations across object categories, previous manipulat…
Leveraging SE(3) Equivariance for Learning 3D Geometric Shape Assembly
Ruihai Wu, Chenrui Tie, Yushi Du +2
Shape assembly aims to reassemble parts (or fragments) into a complete object, which is a common task in our daily life. Different from the semantic part assembly (e.g., assembling…