ALAN: Autonomously Exploring Robotic Agents in the Real World

TL;DR

ALAN is a robotic agent designed for autonomous real-world exploration, combining environment change measurement and uncertainty maximization to efficiently learn manipulation skills with minimal supervision.

Contribution

The paper introduces ALAN, a novel autonomous exploration method that effectively scales to real-world environments using environment-centric and agent-centric signals.

Findings

Efficient exploration in real-world settings

Successful learning of manipulation skills

Ability to perform goal-directed tasks

Abstract

Robotic agents that operate autonomously in the real world need to continuously explore their environment and learn from the data collected, with minimal human supervision. While it is possible to build agents that can learn in such a manner without supervision, current methods struggle to scale to the real world. Thus, we propose ALAN, an autonomously exploring robotic agent, that can perform tasks in the real world with little training and interaction time. This is enabled by measuring environment change, which reflects object movement and ignores changes in the robot position. We use this metric directly as an environment-centric signal, and also maximize the uncertainty of predicted environment change, which provides agent-centric exploration signal. We evaluate our approach on two different real-world play kitchen settings, enabling a robot to efficiently explore and discover manipulation skills, and perform tasks specified via goal images. Website at https://robo-explorer.github.io/