◍wovepaper
SearchResearchersInstitutions
Sign in
researcher

Yunzhu Li

6 papers hereh-index 5294 citations6 works total

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

author position
  • middle author3
  • last author2

Across the 5 of 6 papers where every author was matched, so the position is known.

fields
  • cs.RO4
  • cs.CV2
same name
  • Yunzhu Li — 22 papers, h 25
  • Yunzhu Li — 9 papers, h 4
  • Yunzhu Li — 9 papers, h 4
  • Yunzhu Li — 6 papers, h 4
  • Yunzhu Li — 6 papers, h 6
  • Yunzhu Li — 5 papers, h 5

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
20202026
most citedLearning Physical Graph Representations from Visual Scenes

43 citations · 43 across the 5 of their papers we have counts for

collaborators
Showing cs.ROShow all

4 papers · 1 filter

cs.RO2026

DexAgent: An Agentic Human2Sim2Robot Framework for Dexterous Manipulation with Self-Evolving Tool Library

Youhui Wang, Yunzhu Li, Li Fei-Fei +2

Human videos offer a scalable source of demonstrations for dexterous robot manipulation. However, existing human-to-simulation-to-robot (Human2Sim2Robot) pipelines rely on predefin…

cs.RO2026

IMPASTO: Integrating Model-Based Planning with Learned Dynamics Models for Robotic Oil Painting Reproduction

Yingke Wang, Hao Li, Yifeng Zhu +6

Robotic reproduction of oil paintings using soft brushes and pigments requires force-sensitive control of deformable tools, prediction of brushstroke effects, and multi-step stroke…

cs.RO2024

RoboPack: Learning Tactile-Informed Dynamics Models for Dense Packing

Bo Ai, Stephen Tian, Haochen Shi +4

Tactile feedback is critical for understanding the dynamics of both rigid and deformable objects in many manipulation tasks, such as non-prehensile manipulation and dense packing.…

cs.RO2023

Model-Based Control with Sparse Neural Dynamics

Ziang Liu, Genggeng Zhou, Jeff He +4

Learning predictive models from observations using deep neural networks (DNNs) is a promising new approach to many real-world planning and control problems. However, common DNNs ar…

◍wovepaper

Papers, researchers and institutions, woven together.

Explore
  • Search
  • Researchers
  • Institutions
Account
  • Library
  • Chat
Data
  • arXiv.org
  • Semantic Scholar
  • OpenAlex
  • Latest RSS
AboutContactPrivacyDevelopersllms.txtopenapi.json
Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.