# Unsupervised Learning of Goal Spaces for Intrinsically Motivated Goal Exploration

## TL;DR

This paper introduces a two-stage developmental approach where deep learning is used to learn goal spaces from raw sensor data, enabling more autonomous and effective goal exploration in robots without relying on engineered features.

## Contribution

The paper proposes a novel method combining deep representation learning with goal exploration, removing the need for engineered feature spaces and improving autonomous skill acquisition.

## Key findings

- Learned goal spaces match engineered representations in exploration performance
- Deep learning enables autonomous goal space discovery from raw sensor data
- Approach improves robot skill acquisition in complex environments

## Abstract

Intrinsically motivated goal exploration algorithms enable machines to discover repertoires of policies that produce a diversity of effects in complex environments. These exploration algorithms have been shown to allow real world robots to acquire skills such as tool use in high-dimensional continuous state and action spaces. However, they have so far assumed that self-generated goals are sampled in a specifically engineered feature space, limiting their autonomy. In this work, we propose to use deep representation learning algorithms to learn an adequate goal space. This is a developmental 2-stage approach: first, in a perceptual learning stage, deep learning algorithms use passive raw sensor observations of world changes to learn a corresponding latent space; then goal exploration happens in a second stage by sampling goals in this latent space. We present experiments where a simulated robot arm interacts with an object, and we show that exploration algorithms using such learned representations can match the performance obtained using engineered representations.