Lifelong Machine Learning-based Efficient Robotic Object Perception

This Special Issue is intended to present scholarly papers that address the efficient robotic object perception problem from the perspective of lifelong machine learning. Lifelong machine learning aims to utilize knowledge from past tasks to efficiently and effectively learn new tasks over a lifetime, which is more suitable for robotic learning scenarios. The topics of interest include (but are not limited to) object classification, object detection, semantic segmentation, robot navigation, SLAM, and many others. Contributions from experimental researchers and theorists of high-quality manuscripts are invited for publication in this SI.