# GRP Model for Sensorimotor Learning

## TL;DR

This paper introduces the GRP model, a modular neural architecture inspired by physiology, that learns sub-task policies and switching mechanisms from demonstrations, enabling transfer of control from brain to spinal cord in sensorimotor tasks.

## Contribution

The paper presents a novel modular architecture, the GRP model, which learns sub-task policies and switching mechanisms from unsegmented demonstrations, inspired by physiological neural structures.

## Key findings

- GRP model successfully learns sub-task policies from demonstrations.
- The model enables automatic switching between control policies.
- Transfer of swing leg control from brain to spinal cord demonstrated.

## Abstract

Learning from complex demonstrations is challenging, especially when the demonstration consists of different strategies. A popular approach is to use a deep neural network to perform imitation learning. However, the structure of that deep neural network has to be ``deep" enough to capture all possible scenarios. Besides the machine learning issue, how humans learn in the sense of physiology has rarely been addressed and relevant works on spinal cord learning are rarer. In this work, we develop a novel modular learning architecture, the Generator and Responsibility Predictor (GRP) model, which automatically learns the sub-task policies from an unsegmented controller demonstration and learns to switch between the policies. We also introduce a more physiological based neural network architecture. We implemented our GRP model and our proposed neural network to form a model the transfers the swing leg control from the brain to the spinal cord. Our result suggests that by using the GRP model the brain can successfully transfer the target swing leg control to the spinal cord and the resulting model can switch between sub-control policies automatically.