Robot Learning Simultaneously a Task and How to Interpret Human Instructions

Jonathan Grizou 1, 2, * Manuel Lopes 2, 1 Pierre-Yves Oudeyer 2, 1
* Corresponding author
2 Flowers - Flowing Epigenetic Robots and Systems
Inria Bordeaux - Sud-Ouest, U2IS - Unité d'Informatique et d'Ingénierie des Systèmes
Abstract : This paper presents an algorithm to bootstrap shared understanding in a human-robot interaction scenario where the user teaches a robot a new task using teaching instructions yet unknown to it. In such cases, the robot needs to estimate simultaneously what the task is and the associated meaning of instructions received from the user. For this work, we consider a scenario where a human teacher uses initially unknown spoken words, whose associated unknown meaning is either a feedback (good/bad) or a guidance (go left, right, ...). We present computational results, within an inverse reinforcement learning framework, showing that a) it is possible to learn the meaning of unknown and noisy teaching instructions, as well as a new task at the same time, b) it is possible to reuse the acquired knowledge about instructions for learning new tasks, and c) even if the robot initially knows some of the instructions' meanings, the use of extra unknown teaching instructions improves learning efficiency.
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Jonathan Grizou, Manuel Lopes, Pierre-Yves Oudeyer. Robot Learning Simultaneously a Task and How to Interpret Human Instructions. Joint IEEE International Conference on Development and Learning an on Epigenetic Robotics (ICDL-EpiRob), Aug 2013, Osaka, Japan. ⟨hal-00850703⟩

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