Learning with Skill-based Robot Systems : Combining Planning & Knowledge Representation with Reinforcement Learning
Author
Summary, in English
The usage of robots in industry is transforming. Traditionally, robots have been deployed to automate monotonous tasks through manual programming, excelling in speed and precision yet lacking flexibility. Now, as part of Industry 4.0, the paradigm is shifting towards collaborative robotics, where robots are expected to interact dynamically with their environment and handle non-repetitive tasks. This evolution demands a leap towards flexibility and adaptability at both control and task levels. To address these challenges, the concept of “robot skills” — reusable, parameterizable procedures — emerges as a potentially pivotal building block. The skill-based robot control system SkiROS2 is designed to be robot-agnostic and to represent such skills and the necessary knowledge. This knowledge in the world model describes the robot and the environment, facilitating sophisticated reasoning and task planning capabilities.
Despite these advancements, contact-rich tasks remain a complex endeavor, often challenging to fully encapsulate in predefined models. To overcome this, it is possible to allow robot to learn from experience and improve. This thesis presents an approach for robot control and learning based on behavior trees and reinforcement learning (RL). Our integration of robot skills, knowledge and planning with RL does not only enable robots to proficiently learn and execute contact-rich tasks but also allows for the seamless transfer of learned policies to real-world applications. In a comparison with state-of-the-art RL algorithms we show that this combination of planning and learning demonstrates markedly accelerated learning curves. Furthermore, we can demonstrate that the operators can formulate priors for the optimum to guide and speed up the learning process. An extension of this framework further enables robots to adapt to task variations without the need for relearning from scratch, showcasing the system’s robust adaptability and potential for diverse industrial applications.
Despite these advancements, contact-rich tasks remain a complex endeavor, often challenging to fully encapsulate in predefined models. To overcome this, it is possible to allow robot to learn from experience and improve. This thesis presents an approach for robot control and learning based on behavior trees and reinforcement learning (RL). Our integration of robot skills, knowledge and planning with RL does not only enable robots to proficiently learn and execute contact-rich tasks but also allows for the seamless transfer of learned policies to real-world applications. In a comparison with state-of-the-art RL algorithms we show that this combination of planning and learning demonstrates markedly accelerated learning curves. Furthermore, we can demonstrate that the operators can formulate priors for the optimum to guide and speed up the learning process. An extension of this framework further enables robots to adapt to task variations without the need for relearning from scratch, showcasing the system’s robust adaptability and potential for diverse industrial applications.
Department/s
Publishing year
2024-01-09
Language
English
Full text
- Available as PDF - 18 MB
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Document type
Dissertation
Publisher
Computer Science, Lund University
Topic
- Robotics
Status
Published
Project
- WASP Professor Package: Cognitive Robots for Manufacturing
- Efficient Learning of Robot Skills
- RobotLab LTH
- Robotics and Semantic Systems
Supervisor
ISBN/ISSN/Other
- ISBN: 978-91-8039-884-8
- ISBN: 978-91-8039-885-5
Defence date
2 February 2024
Defence time
10:00
Defence place
Lecture Hall E:1406, building E, Ole Römers väg 3, Faculty of Engineering LTH, Lund University, Lund. The dissertation will be live streamed, but part of the premises is to be excluded from the live stream.
Opponent
- Michael Beetz (Prof.)