Growing from Exploration: A self-exploring framework for robots based on foundation models
This work addresses the challenge of autonomous robot exploration, which is a key issue in robotics for developing more adaptable and intelligent systems, though it appears incremental as it builds on existing foundation model approaches.
The authors tackled the problem of enabling robots to autonomously explore and learn in various environments without human intervention, proposing the GExp framework that uses foundation models for self-exploration and skill acquisition, resulting in robots that can solve complex tasks independently.
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.