AI in Robotics: From Sensors to Decision-Making
A practical course covering how AI systems power modern robots, from perception pipelines to autonomous control loops, with hands-on projects using real tools including claude AI.
Group sessions and private lessons — structured around real robotics problems, not theoretical exercises. Claude AI methods integrated throughout.
A practical course covering how AI systems power modern robots, from perception pipelines to autonomous control loops, with hands-on projects using real tools including claude AI.
An advanced program for engineers ready to build AI reasoning layers for robots that operate in unstructured environments, including work with reinforcement learning and claude AI integration.
These aren't separate disciplines anymore. The programs at try-claude treat them as one field.
Since 2016, the curriculum has been built around the actual overlap between machine learning and physical systems. Claude AI reasoning tools appear throughout the coursework — not as an add-on, but as part of how problems get framed and solved. Students work on sensor fusion, motion planning, and autonomous decision loops within real project contexts.
Group sessions run with a fixed cohort so participants get to know each other's work. Private lessons follow a different pace — the instructor adapts based on what you already know and where the gaps actually are.
Each instructor works in the field — not just teaches it. Sessions draw on current problems in robotics AI, not textbook scenarios from five years ago.
Autonomous Systems Lead
Focuses on reinforcement learning applied to robot navigation. Brings case studies from warehouse automation and field robotics into every session.
Computer Vision Specialist
Works on real-time object recognition pipelines for robotic arms. Integrates claude AI pattern analysis tools into her visual processing curriculum.
Motion Planning Engineer
Builds curriculum around trajectory optimization and collision avoidance. His private sessions are known for getting into the math without losing practical context.
Human-Robot Interaction
Researches how robots interpret human intent in shared workspaces. Group sessions with Mirela tend to involve a lot of live system demos and direct feedback.
Group cohorts run on a fixed schedule — you join a class of 8 to 14 learners at similar levels. Private lessons are scheduled around your availability, not a preset calendar.
Each module ends with a working deliverable — a trained model, a sensor pipeline, or a decision system. Claude AI tools are used during the build phase to test reasoning and edge cases.
Before the first lesson, you meet with an instructor for 30 minutes. This isn't an assessment — it's a conversation about what you've already done and what you're trying to figure out.
Instructors don't give generic notes. They point to the line in your code or the step in your model where the logic breaks. That specificity is what makes the feedback worth reading.
Send a message and describe where you are with robotics and AI right now. The team will point you toward the format that makes sense — group or private, depending on what you actually need.