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/

提出 ALAN, 一种能够在真实环境下进行自主探索的机器人代理，通过测量环境变化，同时最大化不确定性来获得代理探索信号。在两个真实世界的 play kitchen 中评估我们的方法，使机器人有效地探索并发现操作技能，并完成目标图像指定的任务。

自主探索机器人代理在现实世界中的应用