AI in Robotics: From Sensors to Decision-Making
A focused learning experience built around AI in robotics — designed for learners who want to move from concept to practical understanding at their own pace.
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Adaptive learning paths
Each learner's progress is tracked individually. The path adjusts based on what you already know, not a fixed syllabus.
Live instructor sessions
Direct access to instructors during scheduled sessions. Ask questions in real time — not through a ticketing system.
Collaborative group work
Group sessions pair you with peers at a similar stage. Working through robotics problems together builds understanding faster than solo study.
Program structure
What the learning path actually covers — broken into clear stages you can navigate at your own speed.
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Sensor Fusion and Environment Representation
Camera, lidar, and IMU integration. Kalman filters in practice. Building an occupancy grid from scratch.
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Motion Planning Algorithms
A-star, RRT, and when each is appropriate. Implementing a planner in ROS 2. Common failure cases and how to detect them.
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Perception with Neural Networks
Object detection pipelines. Model selection trade-offs. Running inference on edge hardware.
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AI Decision Layers
Behavior trees vs. state machines. Integrating learned policies with deterministic planners.
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Natural Language Control with claude AI
Two-session deep diveConnecting claude to a ROS 2 action server. Parsing ambiguous instructions. Handling failure gracefully.
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Capstone Project
Students design and present an autonomous task pipeline that incorporates at least two AI subsystems covered in the course.
What this program actually involves
Robots do not move on their own. Every action traces back to a decision, and every decision traces back to data processed by an AI system. This course walks through exactly how that chain works.
What the course covers
We start with sensor fusion: how a robot combines camera feeds, lidar, and inertial data into a coherent picture of its environment. From there we move into planning algorithms, then into the AI layers that interpret ambiguous situations. Students work with ROS 2 and Python throughout, so the concepts stay grounded in actual code rather than diagrams.
Where large language models fit in
One underexplored area in robotics education is natural language instruction. We dedicate two sessions to integrating conversational AI into robot control pipelines. Students experiment with claude and claude AI to build a simple command interpreter that translates plain English instructions into structured robot actions. This is not a toy demo. The exercise surfaces real problems around ambiguity, latency, and failure modes that practitioners deal with daily.
Who this is for
The course suits engineers with some Python background who want to move into robotics, and roboticists who want to understand the AI components they are already deploying but did not design. It is not an introduction to programming, and it is not a survey course. Each week focuses on one concrete problem and one set of tools for addressing it.
Realistic expectations
By the end you will have built three working prototypes and understand the architecture of a modern autonomous system. Mastery of any single subsystem takes months of additional practice. This course gives you the foundation and the vocabulary to continue that work independently.
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.
ENROLL NOWProgram details
- Duration
- 8 weeks, 6 hours per week
- Topic area
- AI & Robotics
- Delivery
- Remote — worldwide access