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In this paper, we propose a framework that enables a human teacher to shape arobot behaviour by interactively providing it with unlabeled instructions. Weground the meaning of instruction signals in the task-learning process, and usethem simultaneously for guiding the latter. We implement our framework as amodular architecture, named TICS (Task-Instruction-Contingency-Shaping) thatcombines different information sources: a predefined reward function, humanevaluative feedback and unlabeled instructions. This approach provides a novelperspective for robotic task learning that lies between Reinforcement Learningand Supervised Learning paradigms. We evaluate our framework both in simulationand with a real robot. The experimental results demonstrate the effectivenessof our framework in accelerating the task-learning process and in reducing thenumber of required teaching signals.

Interactively shaping robot behaviour with unlabeled human instructions