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Task-motion Planning via Tree-based Q-learning Approach for Robotic Object Displacement in Cluttered Spaces

Topics: Autonomous Agents; Control and Supervision Systems; Data Based Control and AI; Engineering Applications on Robotics and Automation; Hybrid Systems; Information-Based Models for Control; Machine Learning in Control Applications; Optimization Algorithms; Planning and Scheduling

Authors: Giacomo Golluccio ; Daniele Di Vito ; Alessandro Marino ; Alessandro Bria and Gianluca Antonelli

Affiliation: Department of Electrical and Information Engineering, University of Cassino and Southern Lazio, via Di Biasio 43, 03043 Cassino (FR), Italy

Keyword(s): Motion Planning, Task Planning, Reinforcement Learning.

Abstract: In this paper, a Reinforcement Learning approach to the problem of grasping a target object from clutter by a robotic arm is addressed. A layered architecture is devised to the scope. The bottom layer is in charge of planning robot motion in order to relocate objects while taking into account robot constraints, whereas the top layer takes decision about which obstacles to relocate. In order to generate an optimal sequence of obstacles according to some metrics, a tree is dynamically built where nodes represent sequences of relocated objects and edge weights are updated according to a Q-learning-inspired algorithm. Four different exploration strategies of the solution tree are considered, ranging from a random strategy to a ε-Greedy learning-based exploration. The four strategies are compared based on some predefined metrics and in scenarios with different complexity. The learning-based approaches are able to provide optimal relocation sequences despite the high dimensional search spa ce, with the ε-Greedy strategy showing better performance, especially in complex scenarios. (More)

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Paper citation in several formats:
Golluccio, G., Di Vito, D., Marino, A., Bria, A. and Antonelli, G. (2021). Task-motion Planning via Tree-based Q-learning Approach for Robotic Object Displacement in Cluttered Spaces. In Proceedings of the 18th International Conference on Informatics in Control, Automation and Robotics - ICINCO; ISBN 978-989-758-522-7; ISSN 2184-2809, SciTePress, pages 130-137. DOI: 10.5220/0010542601300137

@conference{icinco21,
author={Giacomo Golluccio and Daniele {Di Vito} and Alessandro Marino and Alessandro Bria and Gianluca Antonelli},
title={Task-motion Planning via Tree-based Q-learning Approach for Robotic Object Displacement in Cluttered Spaces},
booktitle={Proceedings of the 18th International Conference on Informatics in Control, Automation and Robotics - ICINCO},
year={2021},
pages={130-137},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010542601300137},
isbn={978-989-758-522-7},
issn={2184-2809},
}

TY - CONF

JO - Proceedings of the 18th International Conference on Informatics in Control, Automation and Robotics - ICINCO
TI - Task-motion Planning via Tree-based Q-learning Approach for Robotic Object Displacement in Cluttered Spaces
SN - 978-989-758-522-7
IS - 2184-2809
AU - Golluccio, G.
AU - Di Vito, D.
AU - Marino, A.
AU - Bria, A.
AU - Antonelli, G.
PY - 2021
SP - 130
EP - 137
DO - 10.5220/0010542601300137
PB - SciTePress

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