Prediction of human behaviour using imitation learning
Αναγνώριση ανθρώπινης συμπεριφοράς μέσω μιμητικής μάθησης
View/
Keywords
Human behaviour ; Imitation learning ; Generative adversarial imitation learning ; Info GAIL ; Object grasping trajectories ; Trust region policy optimisationAbstract
This thesis explores the use of the Info-GAIL algorithm, which is based on the generative
adversarial imitation learning framework to model modalities of human behaviour towards performing tasks. The goal of this thesis is to use behaviour models learnt through Info-GAIL to predict the modality of executing a specific “object grasping” task. This is done through learning sub-task policies from unsegmented demonstrations of tasks. Specifically, this thesis uses a dataset with trajectories regarding human behaviour towards grasping objects of different sizes in specific . These are pre-processed to correct imperfections and exploited to extract features of trajectory states that are used during training. Then, the implemented method is tested and evaluated utilizing the extracted features. The thesis concludes with a thorough presentation of results and proposals for further work towards using multi-modal imitation learning to predict human behaviour in executing tasks.
Postgraduate Studies Programme
Τεχνητή Νοημοσύνη - Artificial IntelligenceDepartment
Σχολή Τεχνολογιών Πληροφορικής και Επικοινωνιών. Τμήμα Ψηφιακών ΣυστημάτωνCorporate Department
National Centre for Scientific Research "Demokritos"Number of pages
72Language
EnglishCollections
Except where otherwise noted, this item's license is described as
Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ελλάδα
Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ελλάδα