The field of robotics is witnessing significant advancements in imitation learning and human-robot interaction. Researchers are exploring new methods to improve the efficiency and effectiveness of imitation learning, such as analyzing human gaze behavior and leveraging cognitive skills to extract task-relevant cues. Additionally, there is a growing interest in developing autonomous prosthetic hand control systems that can automatically grasp and release objects using camera-based feedback. Noteworthy papers in this area include the development of CLONE, a closed-loop whole-body humanoid teleoperation system that enables precise and coordinated whole-body teleoperation over extended durations. The Robot-Gated Interactive Imitation Learning with Adaptive Intervention Mechanism (AIM) is another significant contribution, which reduces expert monitoring efforts and improves learning efficiency. The EyeRobot system, which learns to look and act with a perception-action loop, is also a notable innovation, demonstrating the emergence of hand-eye coordination behaviors that facilitate manipulation over large workspaces. These advancements have the potential to revolutionize various applications, including robotics, prosthetics, and human-robot collaboration, and pave the way for more sophisticated and autonomous systems.