Skill-based Multi-objective Reinforcement Learning of Industrial Robot Tasks with Planning and Knowledge Integration
(2022) 2022 IEEE International Conference on Robotics and Biomimetics, ROBIO 2022 p.1995-2002- Abstract
In modern industrial settings with small batch sizes it should be easy to set up a robot system for a new task. Strategies exist, e.g. the use of skills, but when it comes to handling forces and torques, these systems often fall short. We introduce an approach that provides a combination of task-level planning with targeted learning of scenario-specific parameters for skill-based systems. We propose the following pipeline: the user provides a task goal in the planning language PDDL, then a plan (i.e., a sequence of skills) is generated and the learnable parameters of the skills are automatically identified, and, finally, an operator chooses reward functions and parameters for the learning process. Two aspects of our methodology are... (More)
In modern industrial settings with small batch sizes it should be easy to set up a robot system for a new task. Strategies exist, e.g. the use of skills, but when it comes to handling forces and torques, these systems often fall short. We introduce an approach that provides a combination of task-level planning with targeted learning of scenario-specific parameters for skill-based systems. We propose the following pipeline: the user provides a task goal in the planning language PDDL, then a plan (i.e., a sequence of skills) is generated and the learnable parameters of the skills are automatically identified, and, finally, an operator chooses reward functions and parameters for the learning process. Two aspects of our methodology are critical: (a) learning is tightly integrated with a knowledge framework to support symbolic planning and to provide priors for learning, (b) using multi-objective optimization. This can help to balance key performance indicators (KPIs) such as safety and task performance since they can often affect each other. We adopt a multi-objective Bayesian optimization approach and learn entirely in simulation. We demonstrate the efficacy and versatility of our approach by learning skill parameters for two different contact-rich tasks. We show their successful execution on a real 7-DOF KUKA-iiwa manipulator and outperform the manual parameterization by human robot operators.
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- author
- Mayr, Matthias LU ; Ahmad, Faseeh LU ; Chatzilygeroudis, Konstantinos ; Nardi, Luigi LU and Krueger, Volker LU
- organization
- publishing date
- 2022
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- Reinforcement Learning, Multi-objective, Knowledge Representation
- host publication
- 2022 IEEE International Conference on Robotics and Biomimetics, ROBIO 2022
- pages
- 8 pages
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- conference name
- 2022 IEEE International Conference on Robotics and Biomimetics, ROBIO 2022
- conference location
- Jinghong, China
- conference dates
- 2022-12-05 - 2022-12-09
- external identifiers
-
- scopus:85147332734
- ISBN
- 9781665481090
- DOI
- 10.1109/ROBIO55434.2022.10011996
- project
- RobotLab LTH
- Efficient Learning of Robot Skills
- Robotics and Semantic Systems
- WASP Professor Package: Cognitive Robots for Manufacturing
- language
- English
- LU publication?
- yes
- id
- 57122c71-3201-4d86-85a9-00b3b51935f3
- date added to LUP
- 2023-02-20 15:18:44
- date last changed
- 2024-03-06 08:44:30
@inproceedings{57122c71-3201-4d86-85a9-00b3b51935f3, abstract = {{<p>In modern industrial settings with small batch sizes it should be easy to set up a robot system for a new task. Strategies exist, e.g. the use of skills, but when it comes to handling forces and torques, these systems often fall short. We introduce an approach that provides a combination of task-level planning with targeted learning of scenario-specific parameters for skill-based systems. We propose the following pipeline: the user provides a task goal in the planning language PDDL, then a plan (i.e., a sequence of skills) is generated and the learnable parameters of the skills are automatically identified, and, finally, an operator chooses reward functions and parameters for the learning process. Two aspects of our methodology are critical: (a) learning is tightly integrated with a knowledge framework to support symbolic planning and to provide priors for learning, (b) using multi-objective optimization. This can help to balance key performance indicators (KPIs) such as safety and task performance since they can often affect each other. We adopt a multi-objective Bayesian optimization approach and learn entirely in simulation. We demonstrate the efficacy and versatility of our approach by learning skill parameters for two different contact-rich tasks. We show their successful execution on a real 7-DOF KUKA-iiwa manipulator and outperform the manual parameterization by human robot operators.</p>}}, author = {{Mayr, Matthias and Ahmad, Faseeh and Chatzilygeroudis, Konstantinos and Nardi, Luigi and Krueger, Volker}}, booktitle = {{2022 IEEE International Conference on Robotics and Biomimetics, ROBIO 2022}}, isbn = {{9781665481090}}, keywords = {{Reinforcement Learning; Multi-objective; Knowledge Representation}}, language = {{eng}}, pages = {{1995--2002}}, publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}}, title = {{Skill-based Multi-objective Reinforcement Learning of Industrial Robot Tasks with Planning and Knowledge Integration}}, url = {{http://dx.doi.org/10.1109/ROBIO55434.2022.10011996}}, doi = {{10.1109/ROBIO55434.2022.10011996}}, year = {{2022}}, }