> ## Documentation Index
> Fetch the complete documentation index at: https://aegean.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Robotics Course Syllabus

> Syllabus for AI for Robotics course.

## Books

1. **THRUN** - [Probabilistic Robotics](https://www.amazon.com/Probabilistic-Robotics-Intelligent-Autonomous-Agents/dp/0262201623) by Sebastian Thrun, Wolfram Burgard, and Dieter Fox, 2005. Required textbook - the foundational text for modern robotics.

2. **LYNCH** - [Modern Robotics: Mechanics, Planning and Control](https://hades.mech.northwestern.edu/images/7/7f/MR.pdf). Free to download. Oriented towards manipulation with foundational motion algebra.

3. **CORKE** - [Robotics, Vision and Control: Fundamental Algorithms in PYTHON](https://a.co/d/07YPiFLp) by Peter Corke, 3rd edition, 2023. Hands-on complement to THRUN and LYNCH. See also the [robotics-toolbox-python](https://github.com/petercorke/robotics-toolbox-python) repository.

## Learning Outcomes

After completing this course, students will be able to:

1. Design the various subsystems involved in robotic agents with egomotion
2. Implement perception using sensor fusion (computer vision with LiDAR and other sensors)
3. Implement planning algorithms for path planning and motion/trajectory planning
4. Train robotic control policies in simulation and transfer them to reality
5. Instruct robots using natural language
6. Program robotic systems using the ROS2 framework

## Planned Schedule

This course emphasizes mobile robots (not manipulation) and covers:

* **Part I:** Robotic Perception (Lectures 1-5)
* **Part II:** Localization and Mapping (Lectures 6-7)
* **Part III:** Task, Global and Local Planning (Lectures 8-9)
* **Part IV:** Reinforcement Learning, Instruction Following (Lectures 10-12)

| Lecture | Topic                 | Description                                                                                                                  |
| ------- | --------------------- | ---------------------------------------------------------------------------------------------------------------------------- |
| **1**   | Introduction          | AI and robotics systems perspective with autonomous vehicles focus. Prerequisites review.                                    |
| **2**   | Perception with DNNs  | Multi-modal sensing, maximum likelihood optimization, fully connected and convolutional architectures.                       |
| **3**   | Object Detection      | MaskRCNN, YOLO, transfer learning with pretrained feature extractors.                                                        |
| **4**   | Segmentation          | Semantic and instance segmentation for complex scene planning.                                                               |
| **5**   | State Estimation      | Recursive estimation, Dynamic Bayesian Networks, Kalman filters. **Reading:** THRUN Chapters 2-3                             |
| **6**   | Localization          | Egomotion, velocity/odometry models, pose estimation. **Reading:** THRUN Chapters 5, 7; CORKE Chapter 4                      |
| **7**   | SLAM                  | Simultaneous Localization and Mapping, Visual SLAM with monocular cameras. **Reading:** THRUN Chapters 9-10; CORKE Chapter 6 |
| **8**   | Global Planning       | Optimal planning under uncertainty, A\*, D\*, RRT\*, PRM algorithms.                                                         |
| **9**   | MDPs and POMDPs       | Sequential decisions, reward signals, Bellman equations.                                                                     |
| **10**  | Deep RL               | Model-free methods for robotic control policies.                                                                             |
| **11**  | Instruction Following | Vision Language Models for human-robot collaboration.                                                                        |
| **12**  | VLA Models            | End-to-end Vision-Language-Action models for instruction parsing, perception, and action.                                    |
| **13**  | Review                | Final exam preparation                                                                                                       |

***

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