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Shuo Yang

9 papers hereh-index 331 citations9 works total

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

author position
  • middle author4
  • last author5

Across the 9 of 9 papers where every author was matched, so the position is known.

fields
  • cs.RO4
  • cs.CV3
  • cs.AI1
  • cs.CL1
same name
  • Shuo Yang — 26 papers, h 11
  • Shuo Yang — 18 papers, h 14
  • Shuo Yang — 11 papers, h 7
  • Shuo Yang — 10 papers, h 4
  • Shuo Yang — 9 papers, h 2
  • Shuo Yang — 9 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

collaborators
Showing cs.ROShow all

4 papers · 1 filter

cs.RO2026

Learning to Accelerate Vision-Language-Action Models through Adaptive Visual Token Caching

Yujie Wei, Jiahan Fan, Jiyu Guo +6

Vision-Language-Action (VLA) models have demonstrated remarkable generalization capabilities in robotic manipulation tasks, yet their substantial computational overhead remains a c…

cs.RO2026

ConLA: Contrastive Latent Action Learning from Human Videos for Robotic Manipulation

Weisheng Dai, Kai Lan, Jianyi Zhou +5

Vision-Language-Action (VLA) models achieve preliminary generalization through pretraining on large scale robot teleoperation datasets. However, acquiring datasets that comprehensi…

cs.RO2026

APEX: A Decoupled Memory-based Explorer for Asynchronous Aerial Object Goal Navigation

Daoxuan Zhang, Ping Chen, Xiaobo Xia +4

Aerial Object Goal Navigation, a challenging frontier in Embodied AI, requires an Unmanned Aerial Vehicle (UAV) agent to autonomously explore, reason, and identify a specific targe…

cs.RO2026

Inject Once Survive Later: Backdooring Vision-Language-Action Models to Persist Through Downstream Fine-tuning

Jianyi Zhou, Yujie Wei, Ruichen Zhen +5

Vision-Language-Action (VLA) models have become foundational to modern embodied AI systems. By integrating visual perception, language understanding, and action planning, they enab…

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