429 citations · 611 across the 14 of their papers we have counts for
16 papers
Force-Modulated Dexterity: Unified Compliance for Contact-Rich Manipulation
Ying Yuan, David Held, Jitendra Malik +1
Teleoperation is widely used to collect high-quality demonstrations for robot learning. Dexterous manipulation, however, requires coordinated control of motion and contact forces.…
GOTT: Object-centric Dexterous Manipulation with a Reusable Cross-Embodiment Primitive
Yulin Liu, Lai Wei, Yen-Jen Wang +4
Foundation models and large-scale human data provide rich sources of manipulation intent, but translating this intent into multi-fingered robot behavior remains difficult. Dexterou…
SemanticFeels: Semantic Labeling during In-Hand Manipulation
Anas Al Shikh Khalil, Haozhi Qi, Roberto Calandra
As robots become increasingly integrated into everyday tasks, their ability to perceive both the shape and properties of objects during in-hand manipulation becomes critical for ad…
General In-Hand Object Rotation with Vision and Touch
Haozhi Qi, Brent Yi, Sudharshan Suresh +4
We introduce RotateIt, a system that enables fingertip-based object rotation along multiple axes by leveraging multimodal sensory inputs. Our system is trained in simulation, where…
In-Hand Object Rotation via Rapid Motor Adaptation
Haozhi Qi, Ashish Kumar, Roberto Calandra +2
Generalized in-hand manipulation has long been an unsolved challenge of robotics. As a small step towards this grand goal, we demonstrate how to design and learn a simple adaptive…
Coupling Vision and Proprioception for Navigation of Legged Robots
Zipeng Fu, Ashish Kumar, Ananye Agarwal +3
We exploit the complementary strengths of vision and proprioception to develop a point-goal navigation system for legged robots, called VP-Nav. Legged systems are capable of traver…
ReduNet: A White-box Deep Network from the Principle of Maximizing Rate Reduction
Kwan Ho Ryan Chan, Yaodong Yu, Chong You +3
This work attempts to provide a plausible theoretical framework that aims to interpret modern deep (convolutional) networks from the principles of data compression and discriminati…
Deep Networks from the Principle of Rate Reduction
Kwan Ho Ryan Chan, Yaodong Yu, Chong You +3
This work attempts to interpret modern deep (convolutional) networks from the principles of rate reduction and (shift) invariant classification. We show that the basic iterative gr…
Learning Long-term Visual Dynamics with Region Proposal Interaction Networks
Haozhi Qi, Xiaolong Wang, Deepak Pathak +2
Learning long-term dynamics models is the key to understanding physical common sense. Most existing approaches on learning dynamics from visual input sidestep long-term predictions…
From A Glance to "Gotcha": Interactive Facial Image Retrieval with Progressive Relevance Feedback
Xinru Yang, Haozhi Qi, Mingyang Li +1
Facial image retrieval plays a significant role in forensic investigations where an untrained witness tries to identify a suspect from a massive pool of images. However, due to the…
Deep Isometric Learning for Visual Recognition
Haozhi Qi, Chong You, Xiaolong Wang +2
Initialization, normalization, and skip connections are believed to be three indispensable techniques for training very deep convolutional neural networks and obtaining state-of-th…
NeurVPS: Neural Vanishing Point Scanning via Conic Convolution
Yichao Zhou, Haozhi Qi, Jingwei Huang +1
We present a simple yet effective end-to-end trainable deep network with geometry-inspired convolutional operators for detecting vanishing points in images. Traditional convolution…
Learning to Reconstruct 3D Manhattan Wireframes from a Single Image
Yichao Zhou, Haozhi Qi, Yuexiang Zhai +4
In this paper, we propose a method to obtain a compact and accurate 3D wireframe representation from a single image by effectively exploiting global structural regularities. Our me…
End-to-End Wireframe Parsing
Yichao Zhou, Haozhi Qi, Yi Ma
We present a conceptually simple yet effective algorithm to detect wireframes in a given image. Compared to the previous methods which first predict an intermediate heat map and th…
Deformable Convolutional Networks
Jifeng Dai, Haozhi Qi, Yuwen Xiong +4
Convolutional neural networks (CNNs) are inherently limited to model geometric transformations due to the fixed geometric structures in its building modules. In this work, we intro…
Fully Convolutional Instance-aware Semantic Segmentation
Yi Li, Haozhi Qi, Jifeng Dai +2
We present the first fully convolutional end-to-end solution for instance-aware semantic segmentation task. It inherits all the merits of FCNs for semantic segmentation and instanc…