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Zhongyu Li

4 papers here

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

author position
  • middle author3
  • last author1

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

fields
  • cs.CV2
  • cs.RO2
ORCID 0000-0001-9198-2158
same name
  • Zhongyu Li — 16 papers
  • Zhongyu Li — 9 papers
  • Zhongyu Li — 5 papers
  • Zhongyu Li — 5 papers
  • Zhongyu Li — 4 papers
  • Zhongyu Li — 2 papers, h 4

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 citedLearning Smooth Humanoid Locomotion through Lipschitz-Constrained Policies

1 citations · 1 across the 4 of their papers we have counts for

collaborators

4 papers

cs.RO2024★ 1 cited

Learning Smooth Humanoid Locomotion through Lipschitz-Constrained Policies

Zixuan Chen, Xialin He, Yen-Jen Wang +8

Reinforcement learning combined with sim-to-real transfer offers a general framework for developing locomotion controllers for legged robots. To facilitate successful deployment in…

cs.RO2024

Berkeley Humanoid: A Research Platform for Learning-based Control

Qiayuan Liao, Bike Zhang, Xuanyu Huang +3

We introduce Berkeley Humanoid, a reliable and low-cost mid-scale humanoid research platform for learning-based control. Our lightweight, in-house-built robot is designed specifica…

cs.CV2024

SAM-Driven Weakly Supervised Nodule Segmentation with Uncertainty-Aware Cross Teaching

Xingyue Zhao, Peiqi Li, Xiangde Luo +3

Automated nodule segmentation is essential for computer-assisted diagnosis in ultrasound images. Nevertheless, most existing methods depend on precise pixel-level annotations by me…

cs.CV2024

Ultrasound Nodule Segmentation Using Asymmetric Learning with Simple Clinical Annotation

Xingyue Zhao, Zhongyu Li, Xiangde Luo +8

Recent advances in deep learning have greatly facilitated the automated segmentation of ultrasound images, which is essential for nodule morphological analysis. Nevertheless, most…

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