Advancing Autonomous Robots: Challenges and Innovations in Open-World Scene Understanding

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, and transportation. The application scope of mobile robots is constantly expanding from traditional close-set scenes to open-world scenarios. 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 to autonomously adapt and respond to various situations. Scene understanding is foundational for robots to perform complex tasks and interact with the real world. Typical scene understanding tasks, including object detection and semantic segmentation, are performed under close-set conditions, where models are trained using offline datasets and then deployed in real-world applications. However, offline datasets cannot cover all real-world cases, resulting in poor generalization ability in robotic applications. In open-world applications, robots face additional challenges such as adaptability to dynamic environments, the design of self-supervised and life-long learning algorithms for long-term operation, and real-time issues caused by massive data collection and limited computing resources.

This Research Topic aims 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, and the adaptability of robots during long-term operation. 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. Additionally, we aim to foster academic exchanges and technological innovation by encouraging more scholars and industry experts to pay attention to and discuss the development of autonomous robot technology.

To gather further insights into the open-world scene understanding tasks for autonomous robots, we welcome articles addressing, but not limited to, the following themes:
- 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