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AI in Robotics

Programs that fit how you learn

Group sessions and private lessons — structured around real robotics problems, not theoretical exercises. Claude AI methods integrated throughout.

AI robotics learning environment with students working on robotic systems

Available Programs

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AI in Robotics: From Sensors to Decision-Making
AI & Robotics

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.

8 weeks, 6 hours per week
$1,490
One-time payment, lifetime access to materials Includes all project datasets, ROS 2 environment setup guide, and three months of instructor Q&A access via the course forum.
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Advanced Autonomous Systems: AI Reasoning in Physical Robots
Advanced Robotics AI

Advanced Autonomous Systems: AI Reasoning in Physical Robots

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.

12 weeks, 8 hours per week
$2,850
One-time payment or 3 monthly installments of $990 Price includes simulation environment licenses, access to GPU compute credits for RL training, and six months of community forum access after course completion.
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AI and robotics — studied together

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.

4 Live instructors per cohort
38+ Countries represented
6 Weeks average group session
1:1 Private lesson option available

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.

The Instructors

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.

Male instructor specializing in autonomous robotics systems

Tobias Feld

Autonomous Systems Lead

Focuses on reinforcement learning applied to robot navigation. Brings case studies from warehouse automation and field robotics into every session.

Female instructor specializing in computer vision for robotics

Anara Seitkali

Computer Vision Specialist

Works on real-time object recognition pipelines for robotic arms. Integrates claude AI pattern analysis tools into her visual processing curriculum.

Male instructor specializing in robotic motion planning

Dariusz Kwak

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.

Female instructor specializing in human-robot interaction

Mirela Vukić

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.

How the learning actually works

01

Pick your format first

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.

03

Projects over lectures

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.

02

An intake session before anything else

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.

04

Feedback that names specific things

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.

Not sure which program fits?

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.

Get in Touch