The use of humanoid robots within the field of neuroscience has gained substantial interest in recent years, specifically as a means to implement and assess biological concepts. This conceptual paper addresses the vital challenge of uncertainty in the context of lifelong bio-inspired sensorimotor learning. Inspired by insights from developmental and neuroscientific studies, we examine the role of self-learning, exploration, and coordination dynamics. Building on principles derived from neural mechanisms and the concept of brain plasticity, we represent a robot’s internal sensorimotor model and its synaptic-like re-organizational changes through dynamic self-organizing maps. We propose a concept that builds on that and distinguishes itself by employing visuo-arm coordination not as an end goal with potential emergent behaviors, but as a feedback controller, emphasizing the integration of an explainable memory-embedded model for continuous sensorimotor self-learning. We illustrate the framework’s potential in dynamic scenarios such as tool use, where enhanced adaptability and fast task resumption after motor perturbations or recurring tool changes provide significant benefits. Verifying a memory entry is significantly quicker than updating the visuo-motor model. Through the concept of a memory-embedded controller, we establish the groundwork for effective and lifelong learning of sensorimotor skills in humanoid robots.