Thesis project: Reinforcement Learning (RL) for Autonomous Forklift Testing
Toyota Material Handling Europe ABSammanfattning
At Toyota Material Handling, students will conduct a Master Thesis focused on using reinforcement learning to generate test scenarios for autonomous forklift software. The project will involve developing and evaluating methods to improve testing efficiency and failure discovery through intelligent scenario generation. The thesis will be conducted at the Mjölby site, with support from the company, and aims to explore the potential of reinforcement learning in enhancing testing processes.Det här erbjuder vi
Opportunity to work on an exploratory research project with real-world applications.Support and guidance from experienced professionals at Toyota Material Handling.Access to existing testing environments and data.Possibility to collaborate with another student on the thesis.
Ansök senast: 2026-10-27
Publicerad: 2026-09-29
Beskrivning
Are you interested in reinforcement learning, autonomous systems, and finding the limits of intelligent software? At Toyota Material Handling, you will investigate how reinforcement learning can be used to automatically generate challenging test scenarios for autonomous forklift software and discover failures and edge cases more efficiently.
In your Master Thesis at Toyota, you will work on:
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.
The thesis will be carried out with support from Toyota Material Handling, including guidance on the existing testing environment, available data, and autopilot system. As this is an exploratory research project, well-founded partial results are considered valuable outcomes, and the goal is not to deliver a production-ready solution.
Validation of autonomous forklift software requires testing across a large number of operating conditions. Warehouse layouts, obstacles, pallet locations, vehicle configurations, traffic, and other environmental factors create a scenario space that is difficult to cover through manually designed test cases alone.
The purpose of this thesis is to investigate whether reinforcement learning can be used to intelligently generate test scenarios. Rather than controlling the forklift, the RL agent selects the conditions under which the autonomous forklift is tested. Based on the outcome of previous tests, the agent learns which areas of the scenario space are more likely to expose failures, unexpected behavior, or challenging edge cases.
The approach will be evaluated against simpler testing strategies to determine whether reinforcement learning can improve failure discovery, test coverage, or testing efficiency. An important outcome will be an objective assessment of when RL provides value and when simpler approaches may be sufficient.
SCOPE
2 persons / 30 ECTS each
Requirements:
For this master thesis, we are looking for students with following educations or equivalent:
M.Sc. in Computer Science or Artificial Intelligence or equivalent
M.Sc. in Robotics or Autonomous Systems or equivalent
M.Sc. in Software Engineering, Computer Engineering, or a related engineering field
A background in machine learning and reinforcement learning, together with good programming skills, is beneficial. Experience or interest in autonomous systems, optimization, mechanical and electrical knowledge, simulation, or software testing is considered an advantage.
Who is Toyota Material Handling?
Toyota Material Handling is a global leader in material handling, and we are making significant investments to meet the needs of the future. At our site in Mjölby, 3,000 employees work across the entire material handling value chain, from development concepts to finished vehicles. Our product range spans from manual hand trucks to autonomous vehicles and innovative energy solutions.
At Toyota Material Handling, we strive to create a friendly, safe and forward-thinking workplace. Our culture is built on Toyota's core values, where respect and consideration guide us in our daily work. Our ambition is to strengthen our competitiveness by increasing diversity across the organization and embracing our differences.
Start
January 2027
Your application is individual. If you plan to do the master thesis together with another student, please specify name of that person. Latest date for application 2026-11-01.
Your application can be written in English or Swedish.
If you have any questions, please contact:
Göktug Celenk, Data Scientist, +46722081776
Josefin Nilsson, HR, josefin.nilsson@toyota-industries.eu
Instagram: ToyotaMHsweden
Linkedin: Toyota Material Handling Manufacturering Sweden AB
#MS
In your Master Thesis at Toyota, you will work on:
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.
The thesis will be carried out with support from Toyota Material Handling, including guidance on the existing testing environment, available data, and autopilot system. As this is an exploratory research project, well-founded partial results are considered valuable outcomes, and the goal is not to deliver a production-ready solution.
Validation of autonomous forklift software requires testing across a large number of operating conditions. Warehouse layouts, obstacles, pallet locations, vehicle configurations, traffic, and other environmental factors create a scenario space that is difficult to cover through manually designed test cases alone.
The purpose of this thesis is to investigate whether reinforcement learning can be used to intelligently generate test scenarios. Rather than controlling the forklift, the RL agent selects the conditions under which the autonomous forklift is tested. Based on the outcome of previous tests, the agent learns which areas of the scenario space are more likely to expose failures, unexpected behavior, or challenging edge cases.
The approach will be evaluated against simpler testing strategies to determine whether reinforcement learning can improve failure discovery, test coverage, or testing efficiency. An important outcome will be an objective assessment of when RL provides value and when simpler approaches may be sufficient.
SCOPE
2 persons / 30 ECTS each
Requirements:
For this master thesis, we are looking for students with following educations or equivalent:
M.Sc. in Computer Science or Artificial Intelligence or equivalent
M.Sc. in Robotics or Autonomous Systems or equivalent
M.Sc. in Software Engineering, Computer Engineering, or a related engineering field
A background in machine learning and reinforcement learning, together with good programming skills, is beneficial. Experience or interest in autonomous systems, optimization, mechanical and electrical knowledge, simulation, or software testing is considered an advantage.
Who is Toyota Material Handling?
Toyota Material Handling is a global leader in material handling, and we are making significant investments to meet the needs of the future. At our site in Mjölby, 3,000 employees work across the entire material handling value chain, from development concepts to finished vehicles. Our product range spans from manual hand trucks to autonomous vehicles and innovative energy solutions.
At Toyota Material Handling, we strive to create a friendly, safe and forward-thinking workplace. Our culture is built on Toyota's core values, where respect and consideration guide us in our daily work. Our ambition is to strengthen our competitiveness by increasing diversity across the organization and embracing our differences.
Start
January 2027
Your application is individual. If you plan to do the master thesis together with another student, please specify name of that person. Latest date for application 2026-11-01.
Your application can be written in English or Swedish.
If you have any questions, please contact:
Göktug Celenk, Data Scientist, +46722081776
Josefin Nilsson, HR, josefin.nilsson@toyota-industries.eu
Instagram: ToyotaMHsweden
Linkedin: Toyota Material Handling Manufacturering Sweden AB
#MS
Ansök till tjänsten
Thesis project: Reinforcement Learning (RL) for Autonomous Forklift Testing
OM FÖRETAGET
Toyota Material Handling Europe AB
