# Skill Acquisition via Automated Multi-Coordinate Cost Balancing

## TL;DR

This paper introduces MCCB, a framework for learning movement skills from demonstrations by encoding in multiple coordinates, optimizing a convex cost function, and learning coordinate weights, demonstrated on handwriting and complex skills.

## Contribution

MCCB is a novel framework that encodes demonstrations in multiple differential coordinates and learns optimal weights for balancing their importance.

## Key findings

- Effective in reproducing handwriting and complex skills
- Outperforms baseline methods in skill acquisition tasks
- Demonstrates robustness across diverse datasets

## Abstract

We propose a learning framework, named Multi-Coordinate Cost Balancing (MCCB), to address the problem of acquiring point-to-point movement skills from demonstrations. MCCB encodes demonstrations simultaneously in multiple differential coordinates that specify local geometric properties. MCCB generates reproductions by solving a convex optimization problem with a multi-coordinate cost function and linear constraints on the reproductions, such as initial, target, and via points. Further, since the relative importance of each coordinate system in the cost function might be unknown for a given skill, MCCB learns optimal weighting factors that balance the cost function. We demonstrate the effectiveness of MCCB via detailed experiments conducted on one handwriting dataset and three complex skill datasets.