Robot Programming from Demonstration, Feedback and Transfer

Yoan Mollard 1 Thibaut Munzer 1 Andrea Baisero 2 Marc Toussaint 2 Manuel Lopes 1
1 Flowers - Flowing Epigenetic Robots and Systems
Inria Bordeaux - Sud-Ouest, U2IS - Unité d'Informatique et d'Ingénierie des Systèmes
Abstract : This paper presents a novel approach for robot instruction for assembly tasks. We consider that robot programming can be made more efficient, precise and intuitive if we leverage the advantages of complementary approaches such as learning from demonstration, learning from feedback and knowledge transfer. Starting from low-level demonstrations of assembly tasks, the system is able to extract a high-level relational plan of the task. A graphical user interface (GUI) allows then the user to iteratively correct the acquired knowledge by refining high-level plans, and low-level geometrical knowledge of the task. This combination leads to a faster programming phase, more precise than just demonstrations, and more intuitive than just through a GUI. A final process allows to reuse high-level task knowledge for similar tasks in a transfer learning fashion. Finally we present a user study illustrating the advantages of this approach.
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Yoan Mollard, Thibaut Munzer, Andrea Baisero, Marc Toussaint, Manuel Lopes. Robot Programming from Demonstration, Feedback and Transfer. IEEE/RSJ International Conference on Intelligent Robots and Systems, Sep 2015, Hamburg, Germany. ⟨hal-01203350⟩

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