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researcher

Huaping Liu

9 papers hereh-index 333 citations11 works total

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

author position
  • middle author3
  • last author5

Across the 8 of 9 papers where every author was matched, so the position is known.

fields
  • cs.RO3
  • cs.CL2
  • cs.LG2
  • cs.CV1
  • cs.HC1
same name
  • Huaping Liu — 34 papers, h 48
  • Huaping Liu — 8 papers, h 4
  • Huaping Liu — 6 papers, h 5
  • Huaping Liu — 6 papers, h 4
  • Huaping Liu — 6 papers, h 5
  • Huaping Liu — 6 papers, h 3

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
20242026
most citedA VLM-based Method for Visual Anomaly Detection in Robotic Scientific Laboratories

3 citations · 3 across the 9 of their papers we have counts for

collaborators
Showing 2025Show all

4 papers · 1 filter

cs.RO2025

CollabVLA: Self-Reflective Vision-Language-Action Model Dreaming Together with Human

Nan Sun, Yongchang Li, Chenxu Wang +2

In this work, we present CollabVLA, a self-reflective vision-language-action framework that transforms a standard visuomotor policy into a collaborative assistant. CollabVLA tackle…

cs.LG2025

Towards Robust Deep Reinforcement Learning against Environmental State Perturbation

Chenxu Wang, Huaping Liu

Adversarial attacks and robustness in Deep Reinforcement Learning (DRL) have been widely studied in various threat models; however, few consider environmental state perturbations,…

cs.CV2025★ 3 cited

A VLM-based Method for Visual Anomaly Detection in Robotic Scientific Laboratories

Shiwei Lin, Chenxu Wang, Xiaozhen Ding +5

In robot scientific laboratories, visual anomaly detection is important for the timely identification and resolution of potential faults or deviations. It has become a key factor i…

cs.LG2025

Trajectory Entropy Reinforcement Learning for Robust Robot Motor Skill Learning

Bang You, Chenxu Wang, Huaping Liu +2

Simplicity is a critical inductive bias for designing data-driven controllers, especially when robustness is important. Despite the impressive results of deep reinforcement learnin…

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