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Yu Xiang

34 papers hereh-index 3110.5k citations53 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author4
  • middle author22
  • last author6

Across the 32 of 34 papers where every author was matched, so the position is known.

fields
  • cs.CV20
  • cs.RO14
same name
  • Yu Xiang — 29 papers, h 14
  • Yu Xiang — 12 papers, h 10
  • Yu Xiang — 11 papers, h 5
  • Yu Xiang — 10 papers, h 6
  • Yu Xiang — 6 papers, h 4
  • Yu Xiang — 6 papers, h 2

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20152026
most citedDeep Object Pose Estimation for Semantic Robotic Grasping of Household Objects

262 citations · 881 across the 33 of their papers we have counts for

collaborators
Showing 2019 · cs.ROShow all

4 papers · 2 filters

cs.RO2019★ 5 cited

Manipulation Trajectory Optimization with Online Grasp Synthesis and Selection

Lirui Wang, Yu Xiang, Dieter Fox

In robot manipulation, planning the motion of a robot manipulator to grasp an object is a fundamental problem. A manipulation planner needs to generate a trajectory of the manipula…

cs.RO2019★ 5 cited

Scaling Local Control to Large-Scale Topological Navigation

Xiangyun Meng, Nathan Ratliff, Yu Xiang +1

Visual topological navigation has been revitalized recently thanks to the advancement of deep learning that substantially improves robot perception. However, the scalability and re…

cs.RO2019★ 14 cited

Self-supervised 6D Object Pose Estimation for Robot Manipulation

Xinke Deng, Yu Xiang, Arsalan Mousavian +3

To teach robots skills, it is crucial to obtain data with supervision. Since annotating real world data is time-consuming and expensive, enabling robots to learn in a self-supervis…

cs.RO2019★ 2 cited

Neural Autonomous Navigation with Riemannian Motion Policy

Xiangyun Meng, Nathan Ratliff, Yu Xiang +1

End-to-end learning for autonomous navigation has received substantial attention recently as a promising method for reducing modeling error. However, its data complexity, especiall…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.