Autonomous robot technology has experienced rapid development in recent years and has been widely applied in many fields, such as industrial automation, medical care, aerospace, transportation, etc. The application scope of mobile robots is constantly expanding from traditional close-set scenes to open-world scenes. An open-world scenario refers to an uncertain, dynamic and complex environment that contains a variety of objects, scenes and tasks. Robots need to have a deep understanding of the environment in order to autonomously adapt and respond to various situations. Therefore, for the application of autonomous robots in open-world scenarios, it is necessary to research and develop new technologies to achieve their autonomy, adaptability and reliability in scene understanding.
Scene understanding is the foundation for robots to perform complex tasks and interact with the environment in real world. Typical scene understanding tasks, including object detection and semantic segmentation, are performed under close-set condition. Models are trained using offline datasets and then deployed in real-world applications. However, offline datasets cannot cover all the cases in real environments, resulting in poor generalization ability in robotic applications.
In open-world applications, robots also face many other challenges in scene understanding, such as adaptability to the dynamic environment, design of self-supervised learning and life-long learning algorithms for long-term-operation robots, real-time issues caused by the massive data collected by robots in real-time and the limited computing resources of robots, etc.
The goal of this Research Topic is to promote the understanding and application of autonomous robots in open-world scenarios. It will focus on the challenges of open-world scene understanding for autonomous robots, such as unknown objects, dynamic and high-occlusion scenes, significant differences in object scale, adaptability of robot during long-term operation, etc.
We also hope to provide a platform for scholars and researchers to deeply explore the application value of mobile robots in open-world scenarios by gathering the latest research results, technological breakthroughs and practical application cases. At the same time, we also hope that through this Research Topic more scholars and industry experts will pay attention to and discuss the development of autonomous robot technology, promote academic exchanges and technological innovation.
The scope of this Research Topic focuses on the open-world scene understanding tasks for autonomous robots such as robust feature extraction, object detection, semantic segmentation, open-world detection and classification and adaptability during long-term operation. Theoretical studies, practical applications and state-of-the-art reviews in the relevant fields are all encouraged to submit.
We welcome submissions on topics including, but not limited to, the following:
• Advances in open-world scene understanding of autonomous robots
• Novel unsupervised representation learning strategies
• Multi-sensor fusion for robust scene understanding
• Object detection
• Semantic segmentation and panoptic segmentation
• Open-set detection and segmentation
• Incremental learning for open-world scene understanding tasks
• Life-long learning strategies for long-term-operation robots
Keywords:
Open-set Object Detection; Open-world Classification; Representation Learning; Life-long Learning; Scene Understanding; Autonomous Robots
Important Note:
All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
Autonomous robot technology has experienced rapid development in recent years and has been widely applied in many fields, such as industrial automation, medical care, aerospace, transportation, etc. The application scope of mobile robots is constantly expanding from traditional close-set scenes to open-world scenes. An open-world scenario refers to an uncertain, dynamic and complex environment that contains a variety of objects, scenes and tasks. Robots need to have a deep understanding of the environment in order to autonomously adapt and respond to various situations. Therefore, for the application of autonomous robots in open-world scenarios, it is necessary to research and develop new technologies to achieve their autonomy, adaptability and reliability in scene understanding.
Scene understanding is the foundation for robots to perform complex tasks and interact with the environment in real world. Typical scene understanding tasks, including object detection and semantic segmentation, are performed under close-set condition. Models are trained using offline datasets and then deployed in real-world applications. However, offline datasets cannot cover all the cases in real environments, resulting in poor generalization ability in robotic applications.
In open-world applications, robots also face many other challenges in scene understanding, such as adaptability to the dynamic environment, design of self-supervised learning and life-long learning algorithms for long-term-operation robots, real-time issues caused by the massive data collected by robots in real-time and the limited computing resources of robots, etc.
The goal of this Research Topic is to promote the understanding and application of autonomous robots in open-world scenarios. It will focus on the challenges of open-world scene understanding for autonomous robots, such as unknown objects, dynamic and high-occlusion scenes, significant differences in object scale, adaptability of robot during long-term operation, etc.
We also hope to provide a platform for scholars and researchers to deeply explore the application value of mobile robots in open-world scenarios by gathering the latest research results, technological breakthroughs and practical application cases. At the same time, we also hope that through this Research Topic more scholars and industry experts will pay attention to and discuss the development of autonomous robot technology, promote academic exchanges and technological innovation.
The scope of this Research Topic focuses on the open-world scene understanding tasks for autonomous robots such as robust feature extraction, object detection, semantic segmentation, open-world detection and classification and adaptability during long-term operation. Theoretical studies, practical applications and state-of-the-art reviews in the relevant fields are all encouraged to submit.
We welcome submissions on topics including, but not limited to, the following:
• Advances in open-world scene understanding of autonomous robots
• Novel unsupervised representation learning strategies
• Multi-sensor fusion for robust scene understanding
• Object detection
• Semantic segmentation and panoptic segmentation
• Open-set detection and segmentation
• Incremental learning for open-world scene understanding tasks
• Life-long learning strategies for long-term-operation robots
Keywords:
Open-set Object Detection; Open-world Classification; Representation Learning; Life-long Learning; Scene Understanding; Autonomous Robots
Important Note:
All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.