# Local Online Motor Babbling: Learning Motor Abundance of A Musculoskeletal Robot Arm

## TL;DR

This paper introduces a local online motor babbling method using CMA-ES for a musculoskeletal robot arm, enabling efficient exploration of motor abundance and providing insights into muscle stiffness and synergy.

## Contribution

It presents a novel approach combining goal babbling with local online motor babbling to learn inverse kinematics and explore motor abundance in a musculoskeletal robot.

## Key findings

- Efficient exploration of motor abundance in the robot arm.
- Insights into muscle stiffness and synergy.
- Successful learning of inverse kinematics for a 10 DoF system.

## Abstract

Motor babbling and goal babbling has been used for sensorimotor learning of highly redundant systems in soft robotics. Recent works in goal babbling has demonstrated successful learning of inverse kinematics (IK) on such systems, and suggests that babbling in the goal space better resolves motor redundancy by learning as few sensorimotor mapping as possible. However, for musculoskeletal robot systems, motor redundancy can be of useful information to explain muscle activation patterns, thus the term motor abundance. In this work, we introduce some simple heuristics to empirically define the unknown goal space, and learn the inverse kinematics of a 10 DoF musculoskeletal robot arm using directed goal babbling. We then further propose local online motor babbling using Covariance Matrix Adaptation Evolution Strategy (CMA-ES), which bootstraps on the collected samples in goal babbling for initialization, such that motor abundance can be queried for any static goal within the defined goal space. The result shows that our motor babbling approach can efficiently explore motor abundance, and gives useful insights in terms of muscle stiffness and synergy.