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Peng Shu

4 papers here

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

author position
  • first author1
  • middle author3

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

fields
  • cs.RO2
  • astro-ph.IM1
  • cs.CL1
ORCID 0000-0002-5970-9992
same name
  • Peng Shu — 2 papers
  • Peng Shu — 2 papers
  • Peng Shu — 1 paper
  • Peng Shu — 1 paper
  • Peng Shu — 1 paper

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

most citedLarge Language Models for Robotics: Opportunities, Challenges, and Perspectives

20 citations · 29 across the 4 of their papers we have counts for

collaborators

4 papers

astro-ph.IM2024★ 2 cited

Machine Learning-Based Identification of Contaminated Images in Light Curves Data Preprocessing

Hui Li, Rong-Wang Li, Peng Shu +1

Attitude is one of the crucial parameters for space objects and plays a vital role in collision prediction and debris removal. Analyzing light curves to determine attitude is the m…

cs.CL2024★ 6 cited

Reasoning before Comparison: LLM-Enhanced Semantic Similarity Metrics for Domain Specialized Text Analysis

Shaochen Xu, Zihao Wu, Huaqin Zhao +7

In this study, we leverage LLM to enhance the semantic analysis and develop similarity metrics for texts, addressing the limitations of traditional unsupervised NLP metrics like RO…

cs.RO2024★ 1 cited

LLMs for Coding and Robotics Education

Peng Shu, Huaqin Zhao, Hanqi Jiang +9

Large language models and multimodal large language models have revolutionized artificial intelligence recently. An increasing number of regions are now embracing these advanced te…

cs.RO2024★ 20 cited

Large Language Models for Robotics: Opportunities, Challenges, and Perspectives

Jiaqi Wang, Zihao Wu, Yiwei Li +17

Large language models (LLMs) have undergone significant expansion and have been increasingly integrated across various domains. Notably, in the realm of robot task planning, LLMs h…

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