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Identifier 000428277
Title Learning from demonstration to accomplish robotic manipulation tasks
Alternative Title Μάθηση μέσω παρατήρησης για την επίτευξη ρομποτικών δράσεων χειρισμού
Author Κοσκινοπούλου, Μαρία Γ.
Thesis advisor Τραχανιάς, Παναγιώτης
Reviewer Αργυρός, Αντώνιος
Κυριακόπουλος, Κώστας
Falco, Pietro
Doulgeri, Zoe
Alexis, Kostas
Ude, Ales
Abstract The current PhD thesis addresses the formulation and implementation of a methodological framework for robot Learning from Demonstration (LfD). The latter refers to methodologies that develop behavioral policies from example state-to-action mappings. To this end, we study the reciprocal interaction of perception and action, in order to teach robots a repertoire of novel action behaviors. Based on that, we design, develop and implement a robust imitation framework, termed IMFO (IMitation Framework by Observation), that facilitates imitation learning and relevant applications in human-robot interaction (HRI) tasks. IMFO can cope with the reproduction of learned (i.e. previously observed) actions, aswell as novel ones. Mapping of human actions to the respective robotic ones is achieved via an indeterminate depiction, termed latent space representation. The latter accomplishes a compact, yet precise abstraction of action trajectories, effectively representing high dimensional raw actions in a low dimensional space. Moreover, throughout this thesis, we examine the role of time in LfD by enhancing the aforementioned framework with the notion of learning both the spatial and temporal characteristics of human motions. Accordingly, learned actions can be subsequently reproduced in the context of more complex time-informed HRI scenarios. Unlike previous LfD methods that cope only with the spatial traits of an action, the formulated scheme effectively encompasses spatial and temporal aspects. Extensive experimentation with a variety of real robotic platforms demonstrates the robustness and applicability of the introduced integrated LfD scheme. Learned actions are reproduced under the high level control of a time-informed task planner. During the implementation of the studied scenarios, temporal and physical constraints may impose speed adaptations in the performed actions. The employed latent space representation readily supports such variations, giving rise to novel actions in the temporal domain. Experimental results demonstrate the effectiveness of the proposed enhanced imitation scheme in the implementation of HRI scenarios. Additionally, a set of well defined evaluation metrics are introduced to assess the validity of the proposed approach considering the temporal and spatial consistency of the reproduced behaviors. A noteworthy extension of the above regards force-based object grasping for executing sensitive manipulation tasks. This is also treated in the current thesis via a novel supervised learning scheme, termed SLF (Supervised Learning for Force-based manipulation). SLF is formulated as a three-stage process: (a) supervised trial-execution in simulation to acquire sufficient training data; (b) training to facilitate grasp learning with suitable robot-arm pose and lifting force; (c) grasp execution in simulation. Subsequently, following sim-to-real transfer, operation in real environments is achieved in addition to simulated ones, generalizing also for objects not included in the trial sessions. The proposed learning scheme is demonstrated in object lifting tasks where the applied force varies for different objects with similar contact friction coefficients, and likewise the grasping pose. Experimental results on the manipulator YuMi show that the robot is able to effectively reproduce demanding lifting and manipulation tasks after learning is accomplished. In summary, our thesis has studied LfD and has contributed with a novel approach that introduced latent space representations to encode the action characteristics. A framework implementation (IMFO) of our approach allowed extensive experimentation and also conduction of HRI scenarios. The inclusion of temporal aspects in our approach enhanced it to cope with complex, real-life interactions. Finally, the extension of IMFO with forcebased grasping facilitated manipulation tasks with sensitive objects.
Language English
Subject Force-based manipulation
Human-Robot interaction
Latent representation
Machine learning
Neural networks
Temporal planning
Λανθάνουσα απεικόνιση χώρου
Μηχανική μάθηση
Issue date 2020-03-27
Collection   Faculty/Department--Faculty of Sciences and Engineering--Department of Computer Science--Doctoral theses
  Type of Work--Doctoral theses
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