59 citations · 220 across the 19 of their papers we have counts for
20 papers
RATE: Risk-Aware Tactile Encoding for Contact-rich Robotic Manipulation
Yuyao Jiang, Haichao Liu, Jiarui Zheng +3
Tactile sensing is particularly valuable for contact-rich robotic manipulation. Recent work has made substantial progress in tactile representation learning for robotic manipulatio…
Data Pyramid for Embodied Manipulation: A Survey
Yifan Ye, Yankai Fu, Yaoxu Lv +26
Multimodal foundation models learned to see and to speak by consuming the whole internet. Embodied agents admit no such shortcut, since they require data that couple observations w…
UniTacVLA: Unified Tactile Understanding and Prediction in Vision Language Action Models
Xidong Zhang, Yichi Zhang, Jiaxin Shi +5
Vision-language-action (VLA) models have achieved strong performance in many robotic manipulation tasks, yet remain limited in contact-rich dexterous manipulation. To overcome this…
TouchThinker: Scaling Tactile Commonsense Reasoning to the Open World with Large-scale Data and Action-aware Representation
Kailin Lyu, Di Wu, Pengwei Zhang +12
Touch is a key modality for embodied agents to understand the physical world. Although recent work has incorporated tactile signals into language systems for tactile commonsense re…
Touch-R1: Reinforcing Touch Reasoning in MLLMs
Yingxin Lai, Yafei Zhou, Fucai Zhu +2
While rule-based reinforcement learning has recently catalyzed explicit reasoning in multimodal models, tactile reasoning remains largely underexplored. Existing tactile-language m…
Dense RGB SLAM with Neural Implicit Maps
Heng Li, Xiaodong Gu, Weihao Yuan +3
There is an emerging trend of using neural implicit functions for map representation in Simultaneous Localization and Mapping (SLAM). Some pioneer works have achieved encouraging r…
Net: Accurate Panorama Depth Estimation on Spherical Surface
Meng Li, Senbo Wang, Weihao Yuan +3
Monocular depth estimation is an ambiguous problem, thus global structural cues play an important role in current data-driven single-view depth estimation methods. Panorama images…
3D Former: Monocular Scene Reconstruction with 3D SDF Transformers
Weihao Yuan, Xiaodong Gu, Heng Li +2
Monocular scene reconstruction from posed images is challenging due to the complexity of a large environment. Recent volumetric methods learn to directly predict the TSDF volume an…
RCP: Recurrent Closest Point for Scene Flow Estimation on 3D Point Clouds
Xiaodong Gu, Chengzhou Tang, Weihao Yuan +3
3D motion estimation including scene flow and point cloud registration has drawn increasing interest. Inspired by 2D flow estimation, recent methods employ deep neural networks to…
NeW CRFs: Neural Window Fully-connected CRFs for Monocular Depth Estimation
Weihao Yuan, Xiaodong Gu, Zuozhuo Dai +2
Estimating the accurate depth from a single image is challenging since it is inherently ambiguous and ill-posed. While recent works design increasingly complicated and powerful net…
MFuseNet: Robust Depth Estimation with Learned Multiscopic Fusion
Weihao Yuan, Rui Fan, Michael Yu Wang +1
We design a multiscopic vision system that utilizes a low-cost monocular RGB camera to acquire accurate depth estimation. Unlike multi-view stereo with images captured at unconstra…
Stereo Matching by Self-supervision of Multiscopic Vision
Weihao Yuan, Yazhan Zhang, Bingkun Wu +4
Self-supervised learning for depth estimation possesses several advantages over supervised learning. The benefits of no need for ground-truth depth, online fine-tuning, and better…
DRO: Deep Recurrent Optimizer for Video to Depth
Xiaodong Gu, Weihao Yuan, Zuozhuo Dai +4
There are increasing interests of studying the video-to-depth (V2D) problem with machine learning techniques. While earlier methods directly learn a mapping from images to depth ma…
Cluster Contrast for Unsupervised Person Re-Identification
Zuozhuo Dai, Guangyuan Wang, Weihao Yuan +3
State-of-the-art unsupervised re-ID methods train the neural networks using a memory-based non-parametric softmax loss. Instance feature vectors stored in memory are assigned pseud…
Self-supervised Object Tracking with Cycle-consistent Siamese Networks
Weihao Yuan, Michael Yu Wang, Qifeng Chen
Self-supervised learning for visual object tracking possesses valuable advantages compared to supervised learning, such as the non-necessity of laborious human annotations and onli…
Active Perception with A Monocular Camera for Multiscopic Vision
Weihao Yuan, Rui Fan, Michael Yu Wang +1
We design a multiscopic vision system that utilizes a low-cost monocular RGB camera to acquire accurate depth estimation for robotic applications. Unlike multi-view stereo with ima…
Multi-Object Rearrangement with Monte Carlo Tree Search:A Case Study on Planar Nonprehensile Sorting
Haoran Song, Joshua A. Haustein, Weihao Yuan +4
In this work, we address a planar non-prehensile sorting task. Here, a robot needs to push many densely packed objects belonging to different classes into a configuration where the…
Towards Learning to Detect and Predict Contact Events on Vision-based Tactile Sensors
Yazhan Zhang, Weihao Yuan, Zicheng Kan +1
In essence, successful grasp boils down to correct responses to multiple contact events between fingertips and objects. In most scenarios, tactile sensing is adequate to distinguis…
Reinforcement Learning in Topology-based Representation for Human Body Movement with Whole Arm Manipulation
Weihao Yuan, Kaiyu Hang, Haoran Song +3
Moving a human body or a large and bulky object can require the strength of whole arm manipulation (WAM). This type of manipulation places the load on the robot's arms and relies o…
Rearrangement with Nonprehensile Manipulation Using Deep Reinforcement Learning
Weihao Yuan, Johannes A. Stork, Danica Kragic +2
Rearranging objects on a tabletop surface by means of nonprehensile manipulation is a task which requires skillful interaction with the physical world. Usually, this is achieved by…