Maxim Likhachev
CMU SCS
Research focus: reinforcement learning in robotics
人工申请预审审计：通过机器人Maxim Likhachev规划深度进行准备：搜索、运动规划、任务规划、多智能体路径规划、学习启发式cs以及操纵。
Who this advisor fits / 什么情况下适合你
- You want ML tied to real data and deployable problems (signal: reinforcement learning in robotics · Robotic Path Planning Algorithms).
- Very attractive for students who want graph-search planning, real robots, and autonomous systems. The placement is especially good for robotics industry and applied autonomy, with a smaller but real f
- 你想把 ML 接到真实数据与落地问题（当前信号：reinforcement learning in robotics · Robotic Path Planning Algorithms）。
- 该课程非常适合希望从事图搜索规划、机器人应用以及自主系统研究的学生。该项目的就业导向特别适用于机器人产业及应用自主领域，同时提供了一条相对务实的学术路径。
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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