Multi-modal integration of dynamic audiovisual patterns for an interactive reinforcement learning scenario

Robots in domestic environments are receiving more attention, especially in scenarios where they should interact with parent-like trainers for dynamically acquiring and refining knowledge. A prominent paradigm for dynamically learning new tasks has been reinforcement learning. However, due to excessive time needed for the learning process, a promising extension has been made by incorporating an external parent-like trainer into the learning cycle in order to scaffold and speed up the apprenticeship using advice about what actions should be performed for achieving a goal. In interactive reinforcement learning, different uni-modal control interfaces have been proposed that are often quite limited and do not take into account multiple sensor modalities. In this paper, we propose the integration of audiovisual patterns to provide advice to the agent using multi-modal information. In our approach, advice can be given using either speech, gestures, or a combination of both. We introduce a neural network-based approach to integrate multi-modal information from uni-modal modules based on their confidence. Results show that multi-modal integration leads to a better performance of interactive reinforcement learning with the robot being able to learn faster with greater rewards compared to uni-modal scenarios.