Multimodal Aggregation Approach for Memory Vision-Voice Indoor Navigation with Meta-Learning
This addresses indoor navigation for robots, but it appears incremental as it builds on existing multimodal and meta-learning approaches.
The paper tackles indoor navigation by integrating vision and voice inputs, using meta-learning to improve adaptability and memory, and reports that their method outperforms state-of-the-art baselines in experiments.
Vision and voice are two vital keys for agents' interaction and learning. In this paper, we present a novel indoor navigation model called Memory Vision-Voice Indoor Navigation (MVV-IN), which receives voice commands and analyzes multimodal information of visual observation in order to enhance robots' environment understanding. We make use of single RGB images taken by a first-view monocular camera. We also apply a self-attention mechanism to keep the agent focusing on key areas. Memory is important for the agent to avoid repeating certain tasks unnecessarily and in order for it to adapt adequately to new scenes, therefore, we make use of meta-learning. We have experimented with various functional features extracted from visual observation. Comparative experiments prove that our methods outperform state-of-the-art baselines.