Incremental Adaptation of Behaviour Trees for Applications in Learning from Demonstration Frameworks
R.D. de Lange (TU Delft - Mechanical Engineering)
Jens Kober – Mentor (TU Delft - Learning & Autonomous Control)
Dimitris Karageorgos – Graduation committee member (Heemskerk Innovative Technology B.V.)
L. Peternel – Graduation committee member (TU Delft - Human-Robot Interaction)
Corrado Pezzato – Graduation committee member (TU Delft - Robot Dynamics)
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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.