AI4R: Artificial Intelligence for Robotics

Georgia Tech OMSCS AI4R (CS 7638) is a full-stack introduction to robotics intelligence: how robots localize, plan, perceive, and control in uncertain environments. This post captures the major ideas I studied in Spring 2025 and how they connect in real systems.

Course Focus

AI4R is about decision-making under uncertainty. The math matters, but the most important takeaway is system thinking: every component (state estimation, mapping, planning, control) depends on consistent assumptions about noise, timing, and model fidelity.

Localization: Knowing Where You Are

Localization is the foundation. A robot must estimate its pose from noisy sensors and imperfect motion models. The class contrasts global localization (no prior) and local tracking (an initial guess) while showing how both are expressed as probabilistic inference.

Key ideas:

• A motion model predicts where the robot could be next.
• A sensor model corrects that prediction with new measurements.
• Uncertainty is carried explicitly instead of being ignored.

Kalman Filters: Linear Estimation That Scales

The Kalman filter provides a clean framework when systems are approximately linear and noise is Gaussian. What makes it powerful is the balance: a prediction step that carries uncertainty forward, and an update step that blends sensor measurements based on their reliability.

The Extended Kalman Filter (EKF) extends this to nonlinear systems via linearization. It is common in robotics because most real sensors and motion models are nonlinear.

Particle Filters: Flexible Estimation

Particle filters represent a distribution with many weighted samples, which makes them flexible for multi-modal or non-Gaussian scenarios. They are also computationally heavier, so real systems often trade accuracy for speed by controlling particle count and resampling strategies.

SLAM: Mapping While Moving

SLAM is the classic robotics loop: you need a map to localize, and localization to build a map. The course explores feature-based, grid-based, and graph-based SLAM, with loop closure as the mechanism that fixes drift over time.

The key systems insight is that SLAM is not just an algorithm; it is a pipeline that depends on reliable data association and timing.

Planning: From Goals to Paths

Planning converts goals into actions. Search-based methods (A*, Dijkstra) guarantee optimality under certain conditions, while sampling-based methods (RRT, PRM) scale better to high-dimensional spaces.

Configuration space design is often the difference between an elegant planner and a brittle one.

Control: Making Plans Real

Robots do not follow idealized paths. Control fills the gap between planning and reality. The course centers on PID control, then expands to more advanced control like MPC, which optimizes actions while respecting constraints.

The practical lesson: even the best planner fails without a robust controller.

Perception and Sensor Fusion

Robots rarely rely on a single sensor. Combining IMU, odometry, cameras, and LIDAR reduces uncertainty and improves robustness. Sensor fusion is less about one perfect algorithm and more about thoughtful integration and calibration.

Planning Under Uncertainty

Real environments are not fully observable. MDPs and POMDPs provide a formal way to plan when the robot cannot see the full state. These models are expensive but clarify how uncertainty should be represented and propagated.

Practical Constraints

AI4R emphasizes that real robots operate under constraints:

• Compute budgets and latency
• Sensor limitations and calibration drift
• Safety and failure modes

Most bugs in robotics are systems bugs, not algorithm bugs.

Course Takeaways

• State estimation and control are inseparable in practice.
• Noise modeling is a first-class design decision.
• Algorithms must be evaluated as part of a system, not in isolation.

This course ties together probability, optimization, and systems engineering in a way that makes robotics feel both rigorous and grounded.