在半自主遥操作中学习并迁移首选辅助策略的知识
Learn and Transfer Knowledge of Preferred Assistance Strategies in Semi-autonomous Telemanipulation
AI总结：
针对遥操作中机器人辅助与人类偏好不匹配的问题，提出偏好感知辅助知识学习方法及跨机器人手结构的知识迁移机制，实验证明其能有效学习偏好并减少训练成本。
摘要：
使机器人在遥操作物体时既能提供有效辅助又能兼顾操作员的指令极具挑战性，因为机器人的辅助动作对人类操作员而言并不总是直观的，且人类行为和偏好有时对机器人来说难以解释。尽管正在开发各种辅助方法以从不同优化角度提升控制质量，但问题仍然在于确定既能满足遥操作任务精细运动约束，又能符合操作员偏好的合适方法。为解决这些问题，我们开发了一种新颖的偏好感知辅助知识学习方法。辅助偏好模型学习人类偏好的辅助方式，而分段模型更新方法在处理人类偏好数据的模糊性时确保了学习的稳定性。这种偏好感知辅助知识使遥操作机器人手能够提供更主动且符合偏好的辅助，以实现操作成功。我们还开发了知识迁移方法，在不同机器人手结构之间迁移偏好知识，以避免大量针对特定机器人的训练。实验分别遥操作3指手和2指手，完成使用、移动和交接杯子的任务。结果表明，这些方法使机器人能够有效学习偏好知识，并允许在机器人之间以较少的训练工作量进行知识迁移。
英文摘要：
Enabling robots to provide effective assistance yet still accommodating the operator's commands for telemanipulation of an object is very challenging because robot's assistive action is not always intuitive for human operators and human behaviors and preferences are sometimes ambiguous for the robot to interpret. Although various assistance approaches are being developed to improve the control quality from different optimization perspectives, the problem still remains in determining the appropriate approach that satisfies the fine motion constraints for the telemanipulation task and preference of the operator. To address these problems, we developed a novel preference-aware assistance knowledge learning approach. An assistance preference model learns what assistance is preferred by a human, and a stagewise model updating method ensures the learning stability while dealing with the ambiguity of human preference data. Such a preference-aware assistance knowledge enables a teleoperated robot hand to provide more active yet preferred assistance toward manipulation success. We also developed knowledge transfer methods to transfer the preference knowledge across different robot hand structures to avoid extensive robot-specific training. Experiments to telemanipulate a 3-finger hand and 2-finger hand, respectively, to use, move, and hand over a cup have been conducted. Results demonstrated that the methods enabled the robots to effectively learn the preference knowledge and allowed knowledge transfer between robots with less training effort.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
