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.

中文翻译：

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使用无标签的人工指令以交互方式塑造机器人行为

在本文中，我们提出了一个框架，该框架使人类教师可以通过交互地向机器人提供未标记的指令来塑造机器人的行为。在任务学习过程中，我们将指令信号的含义作为基础，并同时使用它们来指导后者。我们将我们的框架实现为一个名为TICS（任务，指令，应急状态，成形）的模块化体系结构，该体系结构结合了不同的信息源：预定义的奖励功能，人工评估反馈和未标记的指令。这种方法为介于强化学习和监督学习范式之间的机器人任务学习提供了新颖的视角。我们通过仿真和真实机器人来评估我们的框架。