Forgetting in Robotic Episodic Long-Term Memory

Joana Plewnia, Fabian Peller-Konrad, Tamim Asfour

Abstract: Artificial cognitive architectures traditionally rely on complex memory models to encode, store, and retrieve information. However, the conventional practice of transferring all data from working memory (WM) to long-term memory (LTM) leads to high data volumes and challenges in efficient information processing and access. Deciding what information to retain or discard within a robot’s LTM is particularly challenging since knowledge about future data utilization is absent. Drawing inspiration from human forgetting this paper implements and evaluates novel forgetting techniques that allow consolidation in the robot’s LTM only when new information is encountered. The proposed approach combines fast filtering during data transfer to the robot’s LTM with slower yet more precise forgetting mechanisms that are periodically evaluated for offline data deletion inside the LTM. We compare different mechanisms, utilizing metrics such as data similarity, data age, and consolidation frequency. The efficacy of forgetting techniques is evaluated by comparing their performance in a task where two ARMAR robots search through their LTM for past object locations in episodic ego-centric images and robot state data. Experimental results show that our forgetting techniques significantly reduce the space requirements of a robot’s LTM while maintaining its capacity to successfully perform tasks relying on LTM information. Notably, similarity-based forgetting methods outperform frequency- and time-based approaches. The combination of online frequency-based, online similarity-based, offline similarity-based, and time-based decay methods shows superior performance compared to using individual forgetting strategies.