Learning to Adapt the Parameters of Behavior Trees and Motion Generators (BTMGs) to Task Variations
(2023) IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2023- Abstract
- The ability to learn new tasks and quickly adapt to different variations or dimensions is an important attribute in agile robotics. In our previous work, we have explored Behavior Trees and Motion Generators (BTMGs) as a robot arm policy representation to facilitate the learning and execution of assembly tasks. The current implementation of the BTMGs for a specific task may not be robust to the changes in the environment and may not generalize well to different variations of tasks. We propose to extend the BTMG policy representation with a module that predicts BTMG parameters for a new task variation. To achieve this, we propose a model that combines a Gaussian process and a weighted support vector machine classifier. This model predicts... (More)
- The ability to learn new tasks and quickly adapt to different variations or dimensions is an important attribute in agile robotics. In our previous work, we have explored Behavior Trees and Motion Generators (BTMGs) as a robot arm policy representation to facilitate the learning and execution of assembly tasks. The current implementation of the BTMGs for a specific task may not be robust to the changes in the environment and may not generalize well to different variations of tasks. We propose to extend the BTMG policy representation with a module that predicts BTMG parameters for a new task variation. To achieve this, we propose a model that combines a Gaussian process and a weighted support vector machine classifier. This model predicts the performance measure and the feasibility of the predicted policy with BTMG parameters and task variations as inputs. Using the outputs of the model, we then construct a surrogate reward function that
is utilized within an optimizer to maximize the performance of a task over BTMG parameters for a fixed task variation. To demonstrate the effectiveness of our proposed approach, we conducted experimental evaluations on push and obstacle avoidance tasks in simulation and with a real KUKA iiwa robot. Furthermore, we compared the performance of our approach with four baseline methods. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/ba5621f9-3313-4b69-afa9-8870c57cd5b3
- author
- Ahmad, Faseeh LU ; Mayr, Matthias LU and Krueger, Volker LU
- organization
- publishing date
- 2023
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2023
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- conference name
- IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2023
- conference location
- Detroit, United States
- conference dates
- 2023-10-01 - 2023-10-05
- external identifiers
-
- scopus:85174790851
- ISBN
- 978-1-6654-9191-4
- 978-1-6654-9190-7
- DOI
- 10.1109/IROS55552.2023.10341636
- 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
- ba5621f9-3313-4b69-afa9-8870c57cd5b3
- alternative location
- https://arxiv.org/abs/2303.08209
- date added to LUP
- 2023-11-02 09:45:25
- date last changed
- 2024-04-19 04:47:02
@inproceedings{ba5621f9-3313-4b69-afa9-8870c57cd5b3, abstract = {{The ability to learn new tasks and quickly adapt to different variations or dimensions is an important attribute in agile robotics. In our previous work, we have explored Behavior Trees and Motion Generators (BTMGs) as a robot arm policy representation to facilitate the learning and execution of assembly tasks. The current implementation of the BTMGs for a specific task may not be robust to the changes in the environment and may not generalize well to different variations of tasks. We propose to extend the BTMG policy representation with a module that predicts BTMG parameters for a new task variation. To achieve this, we propose a model that combines a Gaussian process and a weighted support vector machine classifier. This model predicts the performance measure and the feasibility of the predicted policy with BTMG parameters and task variations as inputs. Using the outputs of the model, we then construct a surrogate reward function that<br/>is utilized within an optimizer to maximize the performance of a task over BTMG parameters for a fixed task variation. To demonstrate the effectiveness of our proposed approach, we conducted experimental evaluations on push and obstacle avoidance tasks in simulation and with a real KUKA iiwa robot. Furthermore, we compared the performance of our approach with four baseline methods.}}, author = {{Ahmad, Faseeh and Mayr, Matthias and Krueger, Volker}}, booktitle = {{2023 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2023}}, isbn = {{978-1-6654-9191-4}}, language = {{eng}}, publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}}, title = {{Learning to Adapt the Parameters of Behavior Trees and Motion Generators (BTMGs) to Task Variations}}, url = {{http://dx.doi.org/10.1109/IROS55552.2023.10341636}}, doi = {{10.1109/IROS55552.2023.10341636}}, year = {{2023}}, }