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Xueqian Wang

4 papers hereh-index 323 citations7 works total

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

author position
  • middle author2
  • last author2

Across the 4 of 4 papers where every author was matched, so the position is known.

fields
  • cs.RO3
  • cs.LG1
same name
  • Xueqian Wang — 20 papers, h 15
  • Xueqian Wang — 19 papers, h 30
  • Xueqian Wang — 14 papers, h 8
  • Xueqian Wang — 13 papers, h 1
  • Xueqian Wang — 11 papers, h 10
  • Xueqian Wang — 10 papers, h 20

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
20212025
most citedValue Penalized Q-Learning for Recommender Systems

20 citations · 20 across the 4 of their papers we have counts for

collaborators

4 papers

cs.RO2025

Learning Generalizable Language-Conditioned Cloth Manipulation from Long Demonstrations

Hanyi Zhao, Jinxuan Zhu, Zihao Yan +3

Multi-step cloth manipulation is a challenging problem for robots due to the high-dimensional state spaces and the dynamics of cloth. Despite recent significant advances in end-to-…

cs.RO2024

GSON: A Group-based Social Navigation Framework with Large Multimodal Model

Shangyi Luo, Peng Sun, Ji Zhu +4

With the increasing presence of service robots and autonomous vehicles in human environments, navigation systems need to evolve beyond simple destination reach to incorporate socia…

cs.RO2023

Learning visual-based deformable object rearrangement with local graph neural networks

Yuhong Deng, Xueqian Wang, Lipeng chen

Goal-conditioned rearrangement of deformable objects (e.g. straightening a rope and folding a cloth) is one of the most common deformable manipulation tasks, where the robot needs…

cs.LG2021★ 20 cited

Value Penalized Q-Learning for Recommender Systems

Chengqian Gao, Ke Xu, Kuangqi Zhou +4

Scaling reinforcement learning (RL) to recommender systems (RS) is promising since maximizing the expected cumulative rewards for RL agents meets the objective of RS, i.e., improvi…

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