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Yi-Ling Qiao

16 papers hereh-index 14840 citations25 works total

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

author position
  • first author8
  • middle author8

Across the 16 of 16 papers where every author was matched, so the position is known.

fields
  • cs.GR5
  • cs.RO4
  • cs.CV3
  • cs.LG3
  • quant-ph1
same name
  • Yi-Ling Qiao — 4 papers, h 3

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
20182026
most citedScalable Differentiable Physics for Learning and Control

31 citations · 92 across the 16 of their papers we have counts for

collaborators
Showing cs.ROShow all

4 papers · 1 filter

cs.RO2026

Act, Sense, Act: Learning Active Perception from Large-Scale Egocentric Human Data

Jialiang Li, Yi Qiao, Yunhan Guo +2

Achieving generalizable manipulation in unconstrained environments requires the robot to proactively resolve information uncertainty, i.e., the capability of active perception. How…

cs.RO2025

IndustryNav: Exploring Spatial Reasoning of Embodied Agents in Dynamic Industrial Navigation

Yifan Li, Lichi Li, Anh Dao +15

While Visual Large Language Models (VLLMs) show great promise as embodied agents, they continue to face substantial challenges in spatial reasoning. Existing embodied benchmarks la…

cs.RO2023★ 2 cited

Towards Generalist Robots: A Promising Paradigm via Generative Simulation

Zhou Xian, Theophile Gervet, Zhenjia Xu +3

This document serves as a position paper that outlines the authors' vision for a potential pathway towards generalist robots. The purpose of this document is to share the excitemen…

cs.RO2020★ 1 cited

OF-VO: Efficient Navigation among Pedestrians Using Commodity Sensors

Jing Liang, Yi-Ling Qiao, Tianrui Guan +1

We present a modified velocity-obstacle (VO) algorithm that uses probabilistic partial observations of the environment to compute velocities and navigate a robot to a target. Our s…

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