From Machine Learning to Control Theory: An Autonomous Driving Case Study

13:30-17:00, January 26 @ Foyer 6

Workshop / Overview

This half-day tutorial addresses the emergence of machine learning techniques, specifically reinforcement learning, within the classic control domain for both image-based and non-image based applications. Segway-like platforms and autonomous driving scenarios serve as illustrative models, providing detailed background for deeper understanding of learning-based control approaches.

For the latest on the tutorial's schedule, setup hints, and frequent Q/A, Kindly read the tutorial's web page on: https://w12-Labs.github.io

The hands-on component starts with a code walk-through, to modify and experiment with simulator-based learning algorithms. Subsequently, the simulator-trained models are then deployed on 1:10 scale rovers for testing and evaluation. Furthermore, selected topics such as performance metrics, generalization, and architectural design/debugging concepts will be covered as time allows.

Participants are encouraged to form 2-4 person groups for an end of session competition.

Workshop / Outcome

Participants will be able to acquire sufficient understanding of the machine learning role within the presented classic control frameworks, and the associated real-world scenarios.

Workshop / Difficulty

Intermediate level

Workshop / Prerequisites

  • Fundamentals of Python
  • Fundamentals of PyTorch
  • Fundamentals of machine learning and/or control theory.
  • A notebook with installed Python, PyTorch, and Gym.

Track / Co-organizers

Adam Barclay

Researcher, v12-labs

Alexandra Tiska

Creative Designer, w12-Labs

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