基于Meta偏好学习的多机器人部署用户快速适应
Meta Preference Learning for Fast User Adaptation in Human-Supervisory Multi-Robot Deployments
摘要：
随着多机器人系统（MRS）被广泛应用于各种任务中，例如自然灾害响应和社会安全，人们热切地期望MRS能够无处不在，使得普通用户无需大量训练就可以容易地操作。然而，人类对任务性能和安全性之间的平衡有各种偏好，对MRS控制提出了不同的要求。不遵守偏好会使人们在操作中感到困难，并降低人们使用MRS的意愿。因此，为了提高社会接受度以及性能，迫切需要在触发人类校正之前根据人类偏好调整MRS行为，这增加了认知负荷。提出了一种新的Meta Preference Learning（MPL）方法，使MRS能够快速适应用户偏好。基于Meta学习机制的MPL可以从有限的指令中快速评估人的偏好，然后，基于神经网络的偏好模型调整MRS行为以适应偏好。为了验证方法的有效性，设计了“一个MRS在地震灾害现场搜索受害者”的任务场景; 20个人类用户参与识别偏好为“积极”、“中等”、“保守”;基于用户指导和领域知识，模拟了大约20，000个偏好，以涵盖与“任务质量”、“任务进度”、“机器人安全”相关的不同操作。MPL在偏好适应中的有效性通过减少人类干预的持续时间和频率来验证。
英文摘要：
As multi-robot systems (MRS) are widely used in various tasks such as natural disaster response and social security, people enthusiastically expect an MRS to be ubiquitous that a general user without heavy training can easily operate. However, humans have various preferences on balancing between task performance and safety, imposing different requirements onto MRS control. Failing to comply with preferences makes people feel difficult in operation and decreases human willingness of using an MRS. Therefore, to improve social acceptance as well as performance, there is an urgent need to adjust MRS behaviors according to human preferences before triggering human corrections, which increases cognitive load. In this paper, a novel Meta Preference Learning (MPL) method was developed to enable an MRS to fast adapt to user preferences. MPL based on meta learning mechanism can quickly assess human preferences from limited instructions; then, a neural network based preference model adjusts MRS behaviors for preference adaption. To validate method effectiveness, a task scenario "An MRS searches victims in an earthquake disaster site" was designed; 20 human users were involved to identify preferences as "aggressive", "medium", "reserved"; based on user guidance and domain knowledge, about 20,000 preferences were simulated to cover different operations related to "task quality", "task progress", "robot safety". The effectiveness of MPL in preference adaption was validated by the reduced duration and frequency of human interventions.
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。
