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 v...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 endows robots with the capability of exploring and learning autonomously without human intervention.To achieve this goal,we devise modules including self-exploration,knowledgebase-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 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.Project website:GExp.com.展开更多
基金supported by Shenzhen Key Laboratory of Ubiquitous Data Enabling(No.ZDSYS20220527171406015)Guangdong Innovative and Entrepreneurial Research Team Program(No.2021ZT09L197)+3 种基金Shenzhen Science and Technology Program(No.JCYJ20220530143013030)National Natural Science Foundation of China(No.62104125)Tsinghua Shenzhen International Graduate School-Shenzhen Pengrui Young Faculty Program of Shenzhen Pengrui Foundation(No.SZPR2023005)Shenzhen Higher Education Stable Support Program(No.WDZC20231129093657002).
文摘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 endows robots with the capability of exploring and learning autonomously without human intervention.To achieve this goal,we devise modules including self-exploration,knowledgebase-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 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.Project website:GExp.com.
