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Dongyoung Kim

14 papers hereh-index 7112 citations15 works total

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

author position
  • first author5
  • middle author9

Across the 14 of 14 papers where every author was matched, so the position is known.

fields
  • cs.RO5
  • cs.LG4
  • cs.CV3
  • cs.AI2
same name
  • Dongyoung Kim — 6 papers, h 5
  • Dongyoung Kim — 4 papers, h 10
  • Dongyoung Kim — 2 papers, h 3
  • Dongyoung Kim — 1 paper, h 2
  • Dongyoung Kim — 1 paper, h 1
  • Dongyoung Kim — 1 paper, h 2

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 citedVisual Representation Learning with Stochastic Frame Prediction

1 citations · 1 across the 12 of their papers we have counts for

collaborators
Showing 2026Show all

4 papers · 1 filter

cs.RO2026

RLDX-1 Technical Report

Dongyoung Kim, Huiwon Jang, Myungkyu Koo +65

While Vision-Language-Action models (VLAs) have shown remarkable progress toward human-like generalist robotic policies through the versatile intelligence (i.e. broad scene underst…

cs.CV2026

SpatialBoost: Enhancing Visual Representation through Language-Guided Reasoning

Byungwoo Jeon, Dongyoung Kim, Huiwon Jang +2

Despite the remarkable success of large-scale pre-trained image representation models (i.e., vision encoders) across various vision tasks, they are predominantly trained on 2D imag…

cs.AI2026

RoboAlign: Learning Test-Time Reasoning for Language-Action Alignment in Vision-Language-Action Models

Dongyoung Kim, Sumin Park, Woomin Song +6

Improving embodied reasoning in multimodal-large-language models (MLLMs) is essential for building vision-language-action models (VLAs) on top of them to readily translate multimod…

cs.RO2026

RoboCurate: Harnessing Diversity with Action-Verified Neural Trajectory for Robot Learning

Seungku Kim, Suhyeok Jang, Byungjun Yoon +3

Synthetic data generated by video generative models has shown promise for robot learning as a scalable pipeline, but it often suffers from inconsistent action quality due to imperf…

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