Search Results for author:
Found 19 papers, 11 papers with code
Hand-Object Interaction Pretraining from Videos
We present an approach to learn general robot manipulation priors from 3D hand-object interaction trajectories.
Lessons from Learning to Spin "Pens"
This serves two purposes: 1) pre-training a sensorimotor policy in simulation; 2) conducting open-loop trajectory replay in the real world.
Learning In-Hand Translation Using Tactile Skin With Shear and Normal Force Sensing
We introduce a sensor model for tactile skin that enables zero-shot sim-to-real transfer of ternary shear and binary normal forces.
Learning Visuotactile Skills with Two Multifingered Hands
Two significant challenges exist: the lack of an affordable and accessible teleoperation system suitable for a dual-arm setup with multifingered hands, and the scarcity of multifingered hand hardware equipped with touch sensing.
Twisting Lids Off with Two Hands
Manipulating objects with two multi-fingered hands has been a long-standing challenge in robotics, attributed to the contact-rich nature of many manipulation tasks and the complexity inherent in coordinating a high-dimensional bimanual system.
Neural feels with neural fields: Visuo-tactile perception for in-hand manipulation
Our neural representation driven by multimodal sensing can serve as a perception backbone towards advancing robot dexterity.
General In-Hand Object Rotation with Vision and Touch
We introduce RotateIt, a system that enables fingertip-based object rotation along multiple axes by leveraging multimodal sensory inputs.
In-Hand Object Rotation via Rapid Motor Adaptation
Generalized in-hand manipulation has long been an unsolved challenge of robotics.
Coupling Vision and Proprioception for Navigation of Legged Robots
A safety advisor module adds sensed unexpected obstacles to the occupancy map and environment-determined speed limits to the velocity command generator.
ReduNet: A White-box Deep Network from the Principle of Maximizing Rate Reduction
This work attempts to provide a plausible theoretical framework that aims to interpret modern deep (convolutional) networks from the principles of data compression and discriminative representation.
Deep Networks from the Principle of Rate Reduction
The layered architectures, linear and nonlinear operators, and even parameters of the network are all explicitly constructed layer-by-layer in a forward propagation fashion by emulating the gradient scheme.
Learning Long-term Visual Dynamics with Region Proposal Interaction Networks
Learning long-term dynamics models is the key to understanding physical common sense.
Ranked #1 on
Visual Reasoning
on PHYRE-1B-Within
From A Glance to "Gotcha": Interactive Facial Image Retrieval with Progressive Relevance Feedback
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.
Deep Isometric Learning for Visual Recognition
Initialization, normalization, and skip connections are believed to be three indispensable techniques for training very deep convolutional neural networks and obtaining state-of-the-art performance.
NeurVPS: Neural Vanishing Point Scanning via Conic Convolution
We present a simple yet effective end-to-end trainable deep network with geometry-inspired convolutional operators for detecting vanishing points in images.
Learning to Reconstruct 3D Manhattan Wireframes from a Single Image
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.
End-to-End Wireframe Parsing
We conduct extensive experiments and show that our method significantly outperforms the previous state-of-the-art wireframe and line extraction algorithms.
Ranked #5 on
Line Segment Detection
on wireframe dataset
Deformable Convolutional Networks
Convolutional neural networks (CNNs) are inherently limited to model geometric transformations due to the fixed geometric structures in its building modules.
Ranked #3 on
Vessel Detection
on Vessel detection Dateset
Fully Convolutional Instance-aware Semantic Segmentation
It inherits all the merits of FCNs for semantic segmentation and instance mask proposal.
Ranked #98 on
Instance Segmentation
on COCO test-dev
