no code implementations • CVPR 2024 • Ruihai Wu, Haoran Lu, Yiyan Wang, YuBo Wang, Hao Dong
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 of garment configurations, geometries and deformations.
no code implementations • 4 Apr 2024 • Kairui Ding, Boyuan Chen, Ruihai Wu, Yuyang Li, Zongzheng Zhang, Huan-ang Gao, Siqi Li, Yixin Zhu, Guyue Zhou, Hao Dong, Hao Zhao
Robotic manipulation of ungraspable objects with two-finger grippers presents significant challenges due to the paucity of graspable features, while traditional pre-grasping techniques, which rely on repositioning objects and leveraging external aids like table edges, lack the adaptability across object categories and scenes.
no code implementations • 13 Mar 2024 • ran Xu, Yan Shen, Xiaoqi Li, Ruihai Wu, Hao Dong
To address these challenges, we introduce a comprehensive benchmark, NrVLM, comprising 15 distinct manipulation tasks, containing over 4500 episodes meticulously annotated with fine-grained language instructions.
1 code implementation • 23 Feb 2024 • Hanxiao Jiang, Binghao Huang, Ruihai Wu, Zhuoran Li, Shubham Garg, Hooshang Nayyeri, Shenlong Wang, Yunzhu Li
Robots need to explore their surroundings to adapt to and tackle tasks in unknown environments.
no code implementations • 21 Nov 2023 • Yushi Du, Ruihai Wu, Yan Shen, Hao Dong
More importantly, while many methods could only model a certain kind of joint motion (such as the revolution in the clockwise order), our proposed framework is generic to different kinds of joint motions in that transformation matrix can model diverse kinds of joint motions in the space.
no code implementations • NeurIPS 2023 • Chuanruo Ning, Ruihai Wu, Haoran Lu, Kaichun Mo, Hao Dong
Our framework explicitly estimates the geometric similarity across different categories, identifying local areas that differ from shapes in the training categories for efficient exploration while concurrently transferring affordance knowledge to similar parts of the objects.
1 code implementation • ICCV 2023 • Ruihai Wu, Chenrui Tie, Yushi Du, Yan Zhao, Hao Dong
Shape assembly aims to reassemble parts (or fragments) into a complete object, which is a common task in our daily life.
no code implementations • ICCV 2023 • Ruihai Wu, Chuanruo Ning, Hao Dong
In this paper, we study deformable object manipulation using dense visual affordance, with generalization towards diverse states, and propose a novel kind of foresightful dense affordance, which avoids local optima by estimating states' values for long-term manipulation.
no code implementations • 5 Jul 2022 • Yan Zhao, Ruihai Wu, Zhehuan Chen, Yourong Zhang, Qingnan Fan, Kaichun Mo, Hao Dong
It is essential yet challenging for future home-assistant robots to understand and manipulate diverse 3D objects in daily human environments.
no code implementations • 1 Dec 2021 • Yian Wang, Ruihai Wu, Kaichun Mo, Jiaqi Ke, Qingnan Fan, Leonidas Guibas, Hao Dong
Perceiving and interacting with 3D articulated objects, such as cabinets, doors, and faucets, pose particular challenges for future home-assistant robots performing daily tasks in human environments.
no code implementations • ICLR 2022 • Ruihai Wu, Yan Zhao, Kaichun Mo, Zizheng Guo, Yian Wang, Tianhao Wu, Qingnan Fan, Xuelin Chen, Leonidas Guibas, Hao Dong
In this paper, we propose object-centric actionable visual priors as a novel perception-interaction handshaking point that the perception system outputs more actionable guidance than kinematic structure estimation, by predicting dense geometry-aware, interaction-aware, and task-aware visual action affordance and trajectory proposals.
3 code implementations • ECCV 2020 • Yihao Zhao, Ruihai Wu, Hao Dong
Cycle-consistency loss is a widely used constraint for such problems.
no code implementations • 9 Sep 2019 • Mingqing Xiao, Adam Kortylewski, Ruihai Wu, Siyuan Qiao, Wei Shen, Alan Yuille
Despite deep convolutional neural networks' great success in object classification, it suffers from severe generalization performance drop under occlusion due to the inconsistency between training and testing data.