Thesis project: Reinforcement Learning (RL) for Autonomous Forklift Testing

Stack

Responsibilities

• ▹Develop and evaluate a reinforcement learning approach for generating test scenarios for autonomous forklift software
• ▹Work with an existing simulation and testing environment and explore how reinforcement learning can interact with it
• ▹Define relevant scenario parameters and investigate how the learning process can be guided toward challenging or interesting operating conditions
• ▹Compare reinforcement learning with simpler scenario-generation approaches, such as random testing and other search or optimization methods
• ▹Analyze discovered failure scenarios and evaluate test coverage, failure discovery rate, and testing efficiency
• ▹Investigate whether failure scenarios discovered in simulation can be reproduced and validated on a physical autonomous truck

Requirements

• ▹M.Sc. in Computer Science or Artificial Intelligence or equivalent, M.Sc. in Robotics or Autonomous Systems or equivalent, or M.Sc. in Software Engineering, Computer Engineering, or a related engineering field
• ▹Scope: 2 persons, 30 ECTS each
• ▹Application can be written in English or Swedish

Nice to have

• ▹Background in machine learning and reinforcement learning, together with good programming skills
• ▹Experience or interest in autonomous systems, optimization, mechanical and electrical knowledge, simulation, or software testing

What we offer

• ▹Support from Toyota Material Handling, including guidance on the existing testing environment, available data, and autopilot system

About the company

Toyota Material Handling is a global leader in material handling. At its site in Mjölby, Sweden, 3,000 employees work from development concepts to finished vehicles; the product range spans from manual hand trucks to autonomous vehicles and innovative energy solutions.