Yang Yang

Orcid: 0000-0001-5814-1170

Affiliations:
  • University of Minnesota, Department of Computer Science and Engineering, Minneapolis, MN, USA


According to our database1, Yang Yang authored at least 13 papers between 2020 and 2025.

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

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
Dataset
Other 

Links

Online presence:

On csauthors.net:

Bibliography

2025
Enhancing Embedding Representation Stability in Recommendation Systems with Semantic ID.
CoRR, April, 2025

Enhancing Embedding Representation Stability in Recommendation Systems with Semantic ID.
Proceedings of the Nineteenth ACM Conference on Recommender Systems, 2025

A Parameter-Efficient Tuning Framework for Language-Guided Object Grounding and Robot Grasping.
Proceedings of the IEEE International Conference on Robotics and Automation, 2025

2024
Attribute-Based Robotic Grasping With Data-Efficient Adaptation.
IEEE Trans. Robotics, 2024

2023
IOSG: Image-Driven Object Searching and Grasping.
IROS, 2023

Adversarial Object Rearrangement in Constrained Environments with Heterogeneous Graph Neural Networks.
IROS, 2023

2022
Learning Object Relations with Graph Neural Networks for Target-Driven Grasping in Dense Clutter.
Proceedings of the 2022 International Conference on Robotics and Automation, 2022

Interactive Robotic Grasping with Attribute-Guided Disambiguation.
Proceedings of the 2022 International Conference on Robotics and Automation, 2022

2021
Collision-Aware Target-Driven Object Grasping in Constrained Environments.
Proceedings of the IEEE International Conference on Robotics and Automation, 2021

Learning Visual Affordances with Target-Orientated Deep Q-Network to Grasp Objects by Harnessing Environmental Fixtures.
Proceedings of the IEEE International Conference on Robotics and Automation, 2021

Attribute-Based Robotic Grasping with One-Grasp Adaptation.
Proceedings of the IEEE International Conference on Robotics and Automation, 2021

2020
A Deep Learning Approach to Grasping the Invisible.
IEEE Robotics Autom. Lett., 2020

Learning to Generate 6-DoF Grasp Poses with Reachability Awareness.
Proceedings of the 2020 IEEE International Conference on Robotics and Automation, 2020


  Loading...