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BeBOP-Combining Reactive Planning and Bayesian Optimization to Solve Robotic Manipulation Tasks

Styrud, Jonathan ; Mayr, Matthias LU orcid ; Hellsten, Erik LU orcid ; Krueger, Volker LU orcid and Smith, Christian (2024) 2024 IEEE International Conference on Robotics and Automation, ICRA 2024 p.16459-16466
Abstract

Robotic systems for manipulation tasks are increasingly expected to be easy to configure for new tasks. While in the past, robot programs were often written statically and tuned manually, the current, faster transition times call for robust, modular and interpretable solutions that also allow a robotic system to learn how to perform a task. We propose the method Behavior-based Bayesian Optimization and Planning (BeBOP) that combines two approaches for generating behavior trees: we build the structure using a reactive planner and learn specific parameters with Bayesian optimization. The method is evaluated on a set of robotic manipulation benchmarks and is shown to outperform state-of-the-art reinforcement learning algorithms by being up... (More)

Robotic systems for manipulation tasks are increasingly expected to be easy to configure for new tasks. While in the past, robot programs were often written statically and tuned manually, the current, faster transition times call for robust, modular and interpretable solutions that also allow a robotic system to learn how to perform a task. We propose the method Behavior-based Bayesian Optimization and Planning (BeBOP) that combines two approaches for generating behavior trees: we build the structure using a reactive planner and learn specific parameters with Bayesian optimization. The method is evaluated on a set of robotic manipulation benchmarks and is shown to outperform state-of-the-art reinforcement learning algorithms by being up to 46 times faster while simultaneously being less dependent on reward shaping. We also propose a modification to the uncertainty estimate for the random forest surrogate models that drastically improves the results.

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Please use this url to cite or link to this publication:
author
; ; ; and
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
Bayesian Optimization, Behavior Trees, Robotic manipulation, Task Planning
host publication
2024 IEEE International Conference on Robotics and Automation, ICRA 2024
pages
8 pages
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
conference name
2024 IEEE International Conference on Robotics and Automation, ICRA 2024
conference location
Yokohama, Japan
conference dates
2024-05-13 - 2024-05-17
external identifiers
  • scopus:85190848983
ISBN
9798350384574
DOI
10.1109/ICRA57147.2024.10611468
language
English
LU publication?
yes
id
0366b67b-99d0-4268-b011-1d4c746381b0
date added to LUP
2025-01-16 12:00:39
date last changed
2025-01-16 12:12:24
@inproceedings{0366b67b-99d0-4268-b011-1d4c746381b0,
  abstract     = {{<p>Robotic systems for manipulation tasks are increasingly expected to be easy to configure for new tasks. While in the past, robot programs were often written statically and tuned manually, the current, faster transition times call for robust, modular and interpretable solutions that also allow a robotic system to learn how to perform a task. We propose the method Behavior-based Bayesian Optimization and Planning (BeBOP) that combines two approaches for generating behavior trees: we build the structure using a reactive planner and learn specific parameters with Bayesian optimization. The method is evaluated on a set of robotic manipulation benchmarks and is shown to outperform state-of-the-art reinforcement learning algorithms by being up to 46 times faster while simultaneously being less dependent on reward shaping. We also propose a modification to the uncertainty estimate for the random forest surrogate models that drastically improves the results.</p>}},
  author       = {{Styrud, Jonathan and Mayr, Matthias and Hellsten, Erik and Krueger, Volker and Smith, Christian}},
  booktitle    = {{2024 IEEE International Conference on Robotics and Automation, ICRA 2024}},
  isbn         = {{9798350384574}},
  keywords     = {{Bayesian Optimization; Behavior Trees; Robotic manipulation; Task Planning}},
  language     = {{eng}},
  pages        = {{16459--16466}},
  publisher    = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
  title        = {{BeBOP-Combining Reactive Planning and Bayesian Optimization to Solve Robotic Manipulation Tasks}},
  url          = {{http://dx.doi.org/10.1109/ICRA57147.2024.10611468}},
  doi          = {{10.1109/ICRA57147.2024.10611468}},
  year         = {{2024}},
}