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Adaptable Recovery Behaviors in Robotics : A Behavior Trees and Motion Generators (BTMG) Approach for Failure Management

Ahmad, Faseeh LU ; Mayr, Matthias LU orcid ; Suresh-Fazeela, Sulthan LU and Krueger, Volker LU orcid (2024) 20th IEEE International Conference on Automation Science and Engineering, CASE 2024 p.1815-1822
Abstract

In dynamic operational environments, particularly in collaborative robotics, the inevitability of failures necessitates robust and adaptable recovery strategies. Traditional automated recovery strategies, while effective for predefined scenarios, often lack the flexibility required for on-the-fly task management and adaptation to expected failures. Addressing this gap, we propose a novel approach that models recovery behaviors as adaptable robotic skills, leveraging the Behavior Trees and Motion Generators (BTMG) framework for policy representation. This approach distinguishes itself by employing reinforcement learning (RL) to dynamically refine recovery behavior parameters, enabling a tailored response to a wide array of failure... (More)

In dynamic operational environments, particularly in collaborative robotics, the inevitability of failures necessitates robust and adaptable recovery strategies. Traditional automated recovery strategies, while effective for predefined scenarios, often lack the flexibility required for on-the-fly task management and adaptation to expected failures. Addressing this gap, we propose a novel approach that models recovery behaviors as adaptable robotic skills, leveraging the Behavior Trees and Motion Generators (BTMG) framework for policy representation. This approach distinguishes itself by employing reinforcement learning (RL) to dynamically refine recovery behavior parameters, enabling a tailored response to a wide array of failure scenarios with minimal human intervention. We assess our methodology through a series of progressively challenging scenarios within a peg-in-a-hole task, demonstrating the approach's effectiveness in enhancing operational efficiency and task success rates in collaborative robotics settings. We validate our approach using a dual-arm KUKA robot.

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author
; ; and
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
host publication
2024 IEEE 20th International Conference on Automation Science and Engineering (CASE)
pages
8 pages
publisher
IEEE Computer Society
conference name
20th IEEE International Conference on Automation Science and Engineering, CASE 2024
conference location
Bari, Italy
conference dates
2024-08-28 - 2024-09-01
external identifiers
  • scopus:85208238193
ISBN
9798350358513
DOI
10.1109/CASE59546.2024.10711715
language
English
LU publication?
yes
id
bda16c36-f161-4840-8157-95450eff9992
date added to LUP
2024-12-16 14:24:32
date last changed
2025-04-04 15:09:22
@inproceedings{bda16c36-f161-4840-8157-95450eff9992,
  abstract     = {{<p>In dynamic operational environments, particularly in collaborative robotics, the inevitability of failures necessitates robust and adaptable recovery strategies. Traditional automated recovery strategies, while effective for predefined scenarios, often lack the flexibility required for on-the-fly task management and adaptation to expected failures. Addressing this gap, we propose a novel approach that models recovery behaviors as adaptable robotic skills, leveraging the Behavior Trees and Motion Generators (BTMG) framework for policy representation. This approach distinguishes itself by employing reinforcement learning (RL) to dynamically refine recovery behavior parameters, enabling a tailored response to a wide array of failure scenarios with minimal human intervention. We assess our methodology through a series of progressively challenging scenarios within a peg-in-a-hole task, demonstrating the approach's effectiveness in enhancing operational efficiency and task success rates in collaborative robotics settings. We validate our approach using a dual-arm KUKA robot.</p>}},
  author       = {{Ahmad, Faseeh and Mayr, Matthias and Suresh-Fazeela, Sulthan and Krueger, Volker}},
  booktitle    = {{2024 IEEE 20th International Conference on Automation Science and Engineering (CASE)}},
  isbn         = {{9798350358513}},
  language     = {{eng}},
  pages        = {{1815--1822}},
  publisher    = {{IEEE Computer Society}},
  title        = {{Adaptable Recovery Behaviors in Robotics : A Behavior Trees and Motion Generators (BTMG) Approach for Failure Management}},
  url          = {{http://dx.doi.org/10.1109/CASE59546.2024.10711715}},
  doi          = {{10.1109/CASE59546.2024.10711715}},
  year         = {{2024}},
}