# Learning from Extrapolated Corrections

## TL;DR

This paper explores how robots can learn cost functions from user corrections by extrapolating limited information, demonstrating that non-Euclidean function spaces improve learning accuracy and user perception.

## Contribution

It introduces a novel approach to extrapolate user corrections using online function approximation with non-Euclidean norms, enhancing robot learning from limited guidance.

## Key findings

- Non-Euclidean norms better capture user intent in uncluttered environments
- Using these norms improves the accuracy of learned cost functions
- User perception of robot performance is positively affected

## Abstract

Our goal is to enable robots to learn cost functions from user guidance. Often it is difficult or impossible for users to provide full demonstrations, so corrections have emerged as an easier guidance channel. However, when robots learn cost functions from corrections rather than demonstrations, they have to extrapolate a small amount of information -- the change of a waypoint along the way -- to the rest of the trajectory. We cast this extrapolation problem as online function approximation, which exposes different ways in which the robot can interpret what trajectory the person intended, depending on the function space used for the approximation. Our simulation results and user study suggest that using function spaces with non-Euclidean norms can better capture what users intend, particularly if environments are uncluttered. This, in turn, can lead to the robot learning a more accurate cost function and improves the user's subjective perceptions of the robot.