Intelligent robot is the ultimate goal in the robotics field. Existing works leverage learning-based or optimization-based methods to accomplish human-defined tasks. However, the challenge of enabling robots to explore various environments autonomously remains unresolved. In this work, we propose a framework named GExp, which enables robots to explore and learn autonomously without human intervention. To achieve this goal, we devise modules including self-exploration, knowledge-base-building, and close-loop feedback based on foundation models. Inspired by the way that infants interact with the world, GExp encourages robots to understand and explore the environment with a series of self-generated tasks. During the process of exploration, the robot will acquire skills from beneficial experiences that are useful in the future. GExp provides robots with the ability to solve complex tasks through self-exploration. GExp work is independent of prior interactive knowledge and human intervention, allowing it to adapt directly to different scenarios, unlike previous studies that provided in-context examples as few-shot learning. In addition, we propose a workflow of deploying the real-world robot system with self-learned skills as an embodied assistant.

翻译：智能机器人是机器人领域的终极目标。现有研究利用基于学习或基于优化的方法完成人类定义的任务，然而，如何使机器人自主探索各种环境的挑战仍未解决。本文提出名为GExp的框架，使机器人能够在无需人工干预的情况下自主探索和学习。为实现这一目标，我们基于基础模型设计了包含自我探索、知识库构建和闭环反馈的模块。受婴儿与世界互动方式的启发，GExp通过一系列自主生成的任务鼓励机器人理解并探索环境。在探索过程中，机器人将从有益经验中获取对未来有用的技能。GExp使机器人具备通过自我探索解决复杂任务的能力，且无需依赖先验交互知识或人工干预，可像无需少样本学习中的上下文示例一样直接适配不同场景，这与以往研究不同。此外，我们提出了一套将具备自学习技能的实体机器人系统部署为具身助手的流程。