Yu Xiang

Affiliations:
  • University of Texas at Dallas, Department of Computer Science, Richardson, TX, USA
  • NVIDIA Research, NVIDIA, Seattle, WA, USA
  • University of Washington, School of Computer Science and Engineering, Seattle, WA, USA


According to our database1, Yu Xiang authored at least 45 papers between 2017 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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Links

On csauthors.net:

Bibliography

2024
RobotFingerPrint: Unified Gripper Coordinate Space for Multi-Gripper Grasp Synthesis.
CoRR, 2024

Continual Distillation Learning.
CoRR, 2024

HO-Cap: A Capture System and Dataset for 3D Reconstruction and Pose Tracking of Hand-Object Interaction.
CoRR, 2024

Adapting Pre-Trained Vision Models for Novel Instance Detection and Segmentation.
CoRR, 2024

MultiGripperGrasp: A Dataset for Robotic Grasping from Parallel Jaw Grippers to Dexterous Hands.
CoRR, 2024

Grasping Trajectory Optimization with Point Clouds.
CoRR, 2024

Predictive Task Guidance with Artificial Intelligence in Augmented Reality.
Proceedings of the IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, 2024

RISeg: Robot Interactive Object Segmentation via Body Frame-Invariant Features.
Proceedings of the IEEE International Conference on Robotics and Automation, 2024

Mean Shift Mask Transformer for Unseen Object Instance Segmentation.
Proceedings of the IEEE International Conference on Robotics and Automation, 2024

SceneReplica: Benchmarking Real-World Robot Manipulation by Creating Replicable Scenes.
Proceedings of the IEEE International Conference on Robotics and Automation, 2024

Segment Every Out-of-Distribution Object.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024

Deep Dependency Networks and Advanced Inference Schemes for Multi-Label Classification.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2023
CaptainCook4D: A dataset for understanding errors in procedural activities.
CoRR, 2023

Proto-CLIP: Vision-Language Prototypical Network for Few-Shot Learning.
CoRR, 2023

SCENEREPLICA: Benchmarking Real-World Robot Manipulation by Creating Reproducible Scenes.
CoRR, 2023

Deep Dependency Networks for Multi-Label Classification.
CoRR, 2023

Self-Supervised Unseen Object Instance Segmentation via Long-Term Robot Interaction.
Proceedings of the Robotics: Science and Systems XIX, Daegu, 2023

FewSOL: A Dataset for Few-Shot Object Learning in Robotic Environments.
Proceedings of the IEEE International Conference on Robotics and Automation, 2023

2022
Hierarchical Policies for Cluttered-Scene Grasping With Latent Plans.
IEEE Robotics Autom. Lett., 2022

iCaps: Iterative Category-Level Object Pose and Shape Estimation.
IEEE Robotics Autom. Lett., 2022

HandoverSim: A Simulation Framework and Benchmark for Human-to-Robot Object Handovers.
Proceedings of the 2022 International Conference on Robotics and Automation, 2022

NeuralGrasps: Learning Implicit Representations for Grasps of Multiple Robotic Hands.
Proceedings of the Conference on Robot Learning, 2022

2021
Unseen Object Instance Segmentation for Robotic Environments.
IEEE Trans. Robotics, 2021

PoseRBPF: A Rao-Blackwellized Particle Filter for 6-D Object Pose Tracking.
IEEE Trans. Robotics, 2021

Learning Composable Behavior Embeddings for Long-Horizon Visual Navigation.
IEEE Robotics Autom. Lett., 2021

Hierarchical Policies for Cluttered-Scene Grasping with Latent Plans.
CoRR, 2021

RGB-D Local Implicit Function for Depth Completion of Transparent Objects.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2021

DexYCB: A Benchmark for Capturing Hand Grasping of Objects.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2021

RICE: Refining Instance Masks in Cluttered Environments with Graph Neural Networks.
Proceedings of the Conference on Robot Learning, 8-11 November 2021, London, UK., 2021

Goal-Auxiliary Actor-Critic for 6D Robotic Grasping with Point Clouds.
Proceedings of the Conference on Robot Learning, 8-11 November 2021, London, UK., 2021

2020
DeepIM: Deep Iterative Matching for 6D Pose Estimation.
Int. J. Comput. Vis., 2020

Goal-Auxiliary Actor-Critic for 6D Robotic Grasping with Point Clouds.
CoRR, 2020

Manipulation Trajectory Optimization with Online Grasp Synthesis and Selection.
Proceedings of the Robotics: Science and Systems XVI, 2020

Scaling Local Control to Large-Scale Topological Navigation.
Proceedings of the 2020 IEEE International Conference on Robotics and Automation, 2020

Self-supervised 6D Object Pose Estimation for Robot Manipulation.
Proceedings of the 2020 IEEE International Conference on Robotics and Automation, 2020

LatentFusion: End-to-End Differentiable Reconstruction and Rendering for Unseen Object Pose Estimation.
Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020

Learning RGB-D Feature Embeddings for Unseen Object Instance Segmentation.
Proceedings of the 4th Conference on Robot Learning, 2020

2019
PoseRBPF: A Rao-Blackwellized Particle Filter for 6D Object Pose Tracking.
CoRR, 2019

PoseRBPF: A Rao-Blackwellized Particle Filter for6D Object Pose Estimation.
Proceedings of the Robotics: Science and Systems XV, 2019

Neural Autonomous Navigation with Riemannian Motion Policy.
Proceedings of the International Conference on Robotics and Automation, 2019

Object Discovery in Videos as Foreground Motion Clustering.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019

The Best of Both Modes: Separately Leveraging RGB and Depth for Unseen Object Instance Segmentation.
Proceedings of the 3rd Annual Conference on Robot Learning, 2019

2018
PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes.
Proceedings of the Robotics: Science and Systems XIV, 2018

Deep Object Pose Estimation for Semantic Robotic Grasping of Household Objects.
Proceedings of the 2nd Annual Conference on Robot Learning, 2018

2017
DA-RNN: Semantic Mapping with Data Associated Recurrent Neural Networks.
Proceedings of the Robotics: Science and Systems XIII, 2017


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