Incremental Adaptation of Behaviour Trees for Applications in Learning from Demonstration Frameworks

Master thesis (2022)

Authors

R.D. de Lange Mechanical Engineering

Contributors

J. Kober Learning & Autonomous Control - Mechanical, Maritime and Materials Engineering (mentor)
Dimitris Karageorgos Heemskerk Innovative Technology B.V. (graduation committee member)
L. Peternel Human-Robot Interaction - Mechanical, Maritime and Materials Engineering (graduation committee member)
C. Pezzato Robot Dynamics - Mechanical, Maritime and Materials Engineering (graduation committee member)
Faculty
Mechanical Engineering, Mechanical Engineering
Copyright: © 2022 Rudy de Lange
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Published Date

13-06-2022

Language

English

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Faculty

Mechanical Engineering

Abstract

This thesis proposes the novel Behaviour Tree Update Framework (BTUF) for the initial construction and continuous incremental adaptation of Behaviour Trees (BTs) for applications in Learning from Demonstration (LfD) frameworks to create complex robot behaviours associated with Activities of Daily Living (ADL) without requiring the user to have a programming or engineering background. BTUF implements several methods towards that end. Automatic generation of the fundamental structure of BTs within BTUF allows for easy human operation of the framework's Text-based User Interface (TUI). Saving and loading of constructed trees facilitates easy expansion and reusability of constructed trees. By expanding upon an initial base behaviour, seemingly simple behaviours can be adapted to facilitate novel instances thereupon, increasing the complexity and functionality of the constructed tree over time. Experimental validation in the form of a user study has provided proof-of-concept within simulation and has given insight in the initial overall performance and general system acceptance of BTUF. Future work is recommended for further validation and improvement of the proposed framework to one day realise a real-life application within healthcare robotics.