  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.
