Sehoon Ha
Georgia Tech CS · Assistant Professor
Research focus: robotics · deep reinforcement learning · robot locomotion · character animation
手动审核申请准备：作为深度具备机器人学习/物理AI背景、具备深度RL、运动学、人形、动画、导航或仿真 - 现实控制能力的申请人。
Who this advisor fits / 什么情况下适合你
- You want ML tied to real data and deployable problems (signal: robotics · deep reinforcement learning).
- Applicants should show physical AI execution: RL/control, simulation-to-real, robotics/graphics systems, and evidence that they can debug embodied agents rather than only train models.
- 你想把 ML 接到真实数据与落地问题（当前信号：robotics · deep reinforcement learning）。
- 申请人应展示执行策略 physical AI：RL/控制、仿真至现实、robotics/图形化系统，并提供能够调试具身智能体而非仅针对模型的证据。
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
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