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researcher

L. Fan

21 papers hereh-index 182.5k citations25 works total

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

author position
  • middle author17
  • last author3

Across the 20 of 21 papers where every author was matched, so the position is known.

fields
  • cs.RO15
  • cs.CV3
  • cs.LG2
  • cs.CL1
same name
  • L. Fan — 33 papers, h 17
  • L. Fan — 28 papers, h 19
  • L. Fan — 11 papers, h 2
  • L. Fan — 7 papers, h 12
  • L. Fan — 3 papers, h 0
  • L. Fan — 2 papers, h 8

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
20232026
most citedDrEureka: Language Model Guided Sim-To-Real Transfer

7 citations · 21 across the 21 of their papers we have counts for

collaborators
Showing 2024 · cs.ROShow all

4 papers · 2 filters

cs.RO2024

One-Step Diffusion Policy: Fast Visuomotor Policies via Diffusion Distillation

Zhendong Wang, Zhaoshuo Li, Ajay Mandlekar +9

Diffusion models, praised for their success in generative tasks, are increasingly being applied to robotics, demonstrating exceptional performance in behavior cloning. However, the…

cs.RO2024★ 1 cited

DexMimicGen: Automated Data Generation for Bimanual Dexterous Manipulation via Imitation Learning

Zhenyu Jiang, Yuqi Xie, Kevin Lin +5

Imitation learning from human demonstrations is an effective means to teach robots manipulation skills. But data acquisition is a major bottleneck in applying this paradigm more br…

cs.RO2024

HOVER: Versatile Neural Whole-Body Controller for Humanoid Robots

Tairan He, Wenli Xiao, Toru Lin +9

Humanoid whole-body control requires adapting to diverse tasks such as navigation, loco-manipulation, and tabletop manipulation, each demanding a different mode of control. For exa…

cs.RO2024★ 7 cited

DrEureka: Language Model Guided Sim-To-Real Transfer

Yecheng Jason Ma, William Liang, Hung-Ju Wang +5

Transferring policies learned in simulation to the real world is a promising strategy for acquiring robot skills at scale. However, sim-to-real approaches typically rely on manual…

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