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Interactively shaping robot behaviour with unlabeled human instructions

Abstract : In this paper, we propose a framework that enables a human teacher to shape a robot behaviour by interactively providing it with unlabeled instructions. We ground the meaning of instruction signals in the task-learning process, and use them simultaneously for guiding the latter. We implement our framework as a modular architecture, named TICS (Task-Instruction-Contingency-Shaping) that combines different information sources: a predefined reward function, human evaluative feedback and unlabeled instructions. This approach provides a novel perspective for robotic task learning that lies between Reinforcement Learning and Supervised Learning paradigms. We evaluate our framework both in simulation and with a real robot. The experimental results demonstrate the effectiveness of our framework in accelerating the task-learning process and in reducing the number of required teaching signals.
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Submitted on : Monday, November 9, 2020 - 2:57:48 PM
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Najar et al. - 2020 - Interact...
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Anis Najar, Olivier Sigaud, Mohamed Chetouani. Interactively shaping robot behaviour with unlabeled human instructions. Autonomous Agents and Multi-Agent Systems, Springer Verlag, 2020, 34 (2), ⟨10.1007/s10458-020-09459-6⟩. ⟨hal-02996137⟩



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