Robot Experience

Robot experience research focuses on enabling robots to learn from and utilize their past interactions to improve performance and human-robot interaction. Current efforts concentrate on developing memory systems for storing and retrieving long-term spatiotemporal data, often employing hierarchical representations and large language models for efficient querying and verbalization of experiences. This research is crucial for creating more robust, adaptable, and user-friendly robots, particularly in complex and dynamic environments, impacting fields like autonomous navigation, manipulation, and human-robot collaboration. Furthermore, research is addressing the need for interpretable models and efficient learning methods to reduce the reliance on extensive pre-training data.