Incremental learning of high-level concepts by imitation
This addresses the need for robots to understand human motions and body language for better human-robot interaction, but it is incremental as it builds on existing imitation learning methods.
The paper tackles the problem of enabling robots to learn high-level concepts from human demonstrations by imitation, presenting ILoCI, a model that incrementally abstracts and generalizes multimodal spatio-temporal data, with results showing efficiency in concept acquisition, recognition, and generation on the LASA handwriting benchmark.
Nowadays, robots become a companion in everyday life. To be well-accepted by humans, robots should efficiently understand meanings of their partners' motions and body language, and respond accordingly. Learning concepts by imitation brings them this ability in a user-friendly way. This paper presents a fast and robust model for Incremental Learning of Concepts by Imitation (ILoCI). In ILoCI, observed multimodal spatio-temporal demonstrations are incrementally abstracted and generalized based on both their perceptual and functional similarities during the imitation. In this method, perceptually similar demonstrations are abstracted by a dynamic model of mirror neuron system. An incremental method is proposed to learn their functional similarities through a limited number of interactions with the teacher. Learning all concepts together by the proposed memory rehearsal enables robot to utilize the common structural relations among concepts which not only expedites the learning process especially at the initial stages, but also improves the generalization ability and the robustness against discrepancies between observed demonstrations. Performance of ILoCI is assessed using standard LASA handwriting benchmark data set. The results show efficiency of ILoCI in concept acquisition, recognition and generation in addition to its robustness against variability in demonstrations.