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Learning with Skill-based Robot Systems : Combining Planning & Knowledge Representation with Reinforcement Learning

Mayr, Matthias LU orcid (2024)
Abstract
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... (More)
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. (Less)
Please use this url to cite or link to this publication:
author
supervisor
opponent
  • Prof. Beetz, Michael, University of Bremen, Germany.
organization
publishing date
type
Thesis
publication status
published
subject
pages
286 pages
publisher
Computer Science, Lund University
defense location
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.
defense date
2024-02-02 10:00:00
ISBN
978-91-8039-884-8
978-91-8039-885-5
project
WASP Professor Package: Cognitive Robots for Manufacturing
Efficient Learning of Robot Skills
RobotLab LTH
Robotics and Semantic Systems
language
English
LU publication?
yes
id
da970015-ec87-4140-8904-574867ffea15
date added to LUP
2024-01-09 18:24:14
date last changed
2024-02-16 09:37:01
@phdthesis{da970015-ec87-4140-8904-574867ffea15,
  abstract     = {{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.<br/><br/>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.}},
  author       = {{Mayr, Matthias}},
  isbn         = {{978-91-8039-884-8}},
  language     = {{eng}},
  month        = {{01}},
  publisher    = {{Computer Science, Lund University}},
  school       = {{Lund University}},
  title        = {{Learning with Skill-based Robot Systems : Combining Planning & Knowledge Representation with Reinforcement Learning}},
  url          = {{https://lup.lub.lu.se/search/files/168751722/PhD_Thesis.pdf}},
  year         = {{2024}},
}