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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The TM, IM, and CM are called Models to indicate that these are learning components, in which a model (of the task, instructions and contingency) is learned. By contrast, in the shaping component SC, there is no learning; the shaping method is determined in advance.
We also consider this task for the experiment with the real robot (cf. Sect. 7).
With myopic discounting (\(\gamma =0\)) , the Q-values play the same role as policy parameters in Actor-Critic. So, this method is still compatible with our view about evaluative feedback as information about the policy.
The first author of this paper.
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This work was supported by the Romeo2 project.
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Najar, A., Sigaud, O. & Chetouani, M. Interactively shaping robot behaviour with unlabeled human instructions. Auton Agent Multi-Agent Syst 34, 35 (2020). https://doi.org/10.1007/s10458-020-09459-6
- Interactive machine learning
- Human–robot interaction
- Reinforcement learning
- Unlabeled instructions