About this Research Topic
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
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.