Yuke Zhu
UT Austin CS · Associate Professor
Research focus: robot learning · interactive perception · robot manipulation · robotics
手动审核申请准备：构建基于 robot learning、robot manipulation、interactive perception、computer vision、machine learning、具身化 AI 的拟合图谱，并引用一个可见的实验室主题、校友/录取模式、基准信号、当前学生名单、招募/状态警告或项目领域，并提出一项具体的第一学期研究扩展。
Who this advisor fits / 什么情况下适合你
- You want ML tied to real data and deployable problems (signal: robot learning · interactive perception).
- Applicants should prepare a concrete robotics/vision/ML systems portfolio: robot-learning paper or benchmark, strong code, real/sim robot evidence, and a statement that fits RPL’s general-purpose auto
- 你想把 ML 接到真实数据与落地问题（当前信号：robot learning · interactive perception）。
- 申请人应准备一份具体的 robotics/vision/ML 系统作品集，包括机器人学习论文或基准测试、强有力的代码、真实或模拟机器人证据，以及一段能够契合 RPL 通用自主议程的陈述。
What to watch for / 什么情况下要慎重
- You want pure theory ML with little domain data or cross-disciplinary collaboration.
- 你要的是纯理论 ML、几乎不碰领域数据或跨学科合作。
Public evidence as of 2026-06-10
These are decision-support signals compiled from public evidence (faculty pages, publications, lab sites) to help you ask better questions — not a ranking, rating, or allegation about this advisor. / 以上为基于公开信息整理的择校参考，帮助你提出更好的问题，并非排名、评分或对该导师的指控。
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