Employing a teleoperation system for gathering demonstrations offers the potential for more efficient learning of robot manipulation. However, teleoperating a robot arm equipped with a dexterous hand or gripper, via a teleoperation system presents inherent challenges due to the task's high dimensionality, complexity of motion, and differences between physiological structures. In this study, we introduce a novel system for joint learning between human operators and robots, that enables human operators to share control of a robot end-effector with a learned assistive agent, simplifies the data collection process, and facilitates simultaneous human demonstration collection and robot manipulation training. As data accumulates, the assistive agent gradually learns. Consequently, less human effort and attention are required, enhancing the efficiency of the data collection process. It also allows the human operator to adjust the control ratio to achieve a trade-off between manual and automated control. We conducted experiments in both simulated environments and physical real-world settings. Through user studies and quantitative evaluations, it is evident that the proposed system could enhance data collection efficiency and reduce the need for human adaptation while ensuring the collected data is of sufficient quality for downstream tasks. \textit{For more details, please refer to our webpage https://norweig1an.github.io/HAJL.github.io/.

翻译：利用遥操作系统收集演示数据为机器人操作学习提供了更高效的潜力。然而，通过遥操作系统操控配备灵巧手或抓取器的机械臂存在固有挑战，这源于任务的高维度特性、运动复杂性以及生理结构差异。本研究提出了一种新型人机协同学习系统，使操作人员能够与习得的辅助智能体共享机器人末端执行器的控制权，从而简化数据收集流程，并实现人类演示采集与机器人操作训练的同步进行。随着数据不断积累，辅助智能体逐步学习。因此，所需的人力投入与注意力随之减少，显著提升了数据收集效率。该系统还允许操作人员调节控制比例，实现人工控制与自动化控制之间的权衡。我们在仿真环境与物理现实场景中均进行了实验。通过用户研究与定量评估表明，所提出的系统能够有效提升数据收集效率，降低对人类适应性的需求，同时确保所收集数据对下游任务具有足够质量。\textit{更多细节请参阅我们的网页 https://norweig1an.github.io/HAJL.github.io/。}