Danfei Xu
Georgia Tech CS · Assistant Professor
Research focus: robot learning · robot reasoning · learning from human data · generative models for robotics
人工审核申请准备：作为机器人学习申请人，需具备感知、人类数据、规划与推理能力，在一体化系统中实现感知、人类数据、规划与推理的无缝连接。
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 · robot reasoning).
- Applicants should bring robot-learning systems work: egocentric/human data, generative task-and-motion planning, imitation learning, active perception, or full-stack robot software.
- 你想把 ML 接到真实数据与落地问题（当前信号：robot learning · robot reasoning）。
- 申请人应提交机器人学习系统的工作成果，包括但不限于基于人/机器人数据的自中心化/人类数据、生成任务与运动规划、imit化学习、主动感知以及完整的机器人软件栈。
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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