# Autonomous Goal Exploration using Learned Goal Spaces for Visuomotor Skill Acquisition in Robots

## TL;DR

This paper demonstrates how robots can autonomously learn visuomotor skills by discovering goal spaces through deep learning, enabling efficient skill acquisition without human supervision in real-world settings.

## Contribution

It introduces a method for autonomous goal exploration using learned goal spaces directly from robot experience, advancing lifelong learning in robotics.

## Key findings

- Successful real-world robotic manipulation of a ball using learned goal spaces
- Effective autonomous skill discovery without prior engineered features
- Demonstrated applicability of deep representation learning in robotics

## Abstract

The automatic and efficient discovery of skills, without supervision, for long-living autonomous agents, remains a challenge of Artificial Intelligence. Intrinsically Motivated Goal Exploration Processes give learning agents a human-inspired mechanism to sequentially select goals to achieve. This approach gives a new perspective on the lifelong learning problem, with promising results on both simulated and real-world experiments. Until recently, those algorithms were restricted to domains with experimenter-knowledge, since the Goal Space used by the agents was built on engineered feature extractors. The recent advances of deep representation learning, enables new ways of designing those feature extractors, using directly the agent experience. Recent work has shown the potential of those methods on simple yet challenging simulated domains. In this paper, we present recent results showing the applicability of those principles on a real-world robotic setup, where a 6-joint robotic arm learns to manipulate a ball inside an arena, by choosing goals in a space learned from its past experience.