Interactive online multimodal association for internal concept building in humanoids

摘要

In this paper we report the results of our research on learning and developing cognitive systems. The results are integrated into ALIS 3, our Autonomous Learning and Interacting System version 3 realized the humanoid robot ASIMO. The results presented address crucial issues in autonomously acquiring mental concepts in artifacts. The major contributions are the following: We researched distributed learning in various modalities in which the local learning decisions mutually support each other. Associations between the different modalities (speech, vision, behavior) are learnt online, thus addressing the issue of grounding semantics. The data from the different modalities is uniformly represented in a hybrid data representation for global decisions and local novelty detection. On the behavior generation side proximity sensor driven reflexive grasping and releasing have been integrated with a planning approach based on whole body motion control. The feasibility of the chosen approach is demonstrated in interactive experiments with the integrated system. The system interactively learns visually defined classes like ¿left¿, ¿right¿, ¿up¿, ¿down¿, ¿large¿, ¿small¿, learns corresponding auditory labels and creates associations linking the auditory labels to the visually defined classes or basic behaviors for building internal concepts.