Goal Oriented Behavior With a Habit-Based Adaptive Sensorimotor Map Network.
Frontiers in neurorobotics January 1, 2022 Felix M G Woolford, Matthew D Egbert 3 citations
A new robot controller model called an ASM-network, built from adaptive sensorimotor maps, enables a robot to learn object discrimination without explicit representations or external rewards. The model combines a mechanism that generates continuous motor activity from past sensorimotor trajectories with an evaluative mechanism that reinforces trajectories supporting higher-order sensorimotor coordinations. In a minimal cognition task, a single robot learned through random exploration and repetition of supportive trajectories. The results demonstrate that recognizable learning behavior can emerge from enactive principles, adapting based on the internal requirements of the action-generating mechanism.