Interactively shaping robot behaviour with unlabeled human instructions

By Anis Najar, Olivier Sigaud, Mohamed Chetouani. 2019

23 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Control and Systems Engineering, Reinforcement Learning

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Citations in the corpus

Inner Monologue: Embodied Reasoning through Planning with Language Models (2022, background): “Some works have been trained directly for language-based interaction in robotic tasks [67, 68, 69, 70].”