Anirudha Majumdar introduces the technical foundations behind modern robotics, from generalist vision-language-action models to the basic sense-think-act feedback loop. He compares definitions of a robot and uses edge cases to explore embodiment, autonomy and task complexity. The course connects motion planning and control with localization, mapping, imitation learning and reinforcement learning.
Anirudha Majumdar treats uncertainty as the common thread across these topics. Drone-delivery, humanoid and autonomous-driving failure clips prompt discussion of localization, perception, dynamics and human behavior. He explicitly presents possible explanations, not verified accident causes, and connects simulation-to-reality mismatch with the difficulty of transferring learned control to hardware.
Anirudha Majumdar outlines a drone-navigation project that builds toward a learned visual policy. He also discusses the tension between becoming proficient with generative AI and retaining technical fundamentals. A research conversation in which a model confidently reverses its conclusion illustrates why domain knowledge matters. His course permits specified learning uses and broad AI assistance for team hardware work, rather than treating the agent as a replacement for understanding.
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