Nov 30, 2021
Jun Tani: The self-Organizing Functional Hierarchy: a neuro-robotics study — Part 1
Posted by Dan Breeden in categories: neuroscience, robotics/AI
The current talk addresses a crucial problem on how compositionality can be naturally developed in cognitive agents by having iterative sensory-motor interactions with the environment.
The talk highlights a dynamic neural network model, so-called the multiple timescales recurrent neural network (MTRNN) model, which has been applied to a set of experiments on developmental learning of compositional actions performed by a humanoid robot made by Sony. The experimental results showed that a set of reusable behavior primitives were developed in the lower level network that is characterized by its fast timescale dynamics while sequential combinations of these primitives were learned in the higher level, which is characterized by its slow timescale dynamics.