  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/
