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/

翻译：在真实世界中自主运行的机器人智能体需要持续探索其环境并从收集的数据中学习，且仅需最少的人类监督。尽管可以构建无需监督即可以此方式学习的智能体，但现有方法难以扩展到真实世界。因此，我们提出ALAN——一种自主探索的机器人智能体，它能够以极少的训练和交互时间在真实世界中执行任务。这一能力通过测量环境变化来实现，该变化反映物体移动而忽略机器人位置的变化。我们直接将该度量作为以环境为中心的信号，并最大化预测环境变化的不确定性，从而提供以智能体为中心的探索信号。我们在两种不同的真实世界玩具厨房场景中评估了我们的方法，使机器人能够高效探索并发现操作技能，同时执行通过目标图像指定的任务。网站：https://robo-explorer.github.io/