CAREER: Learning and Leveraging Conventions in the Design of an Adaptive Haptic Shared Control for Steering a Semi-Automated Vehicle

人类可能会倾向于采用不同的策略来解决冲突。然而，目前用于半自动车辆的控制转移的解决方案主要是基于预定义的规则设计的，并且没有个性化的自动化策略来解决冲突。因此，这些解决方案面临着移交时间延长以及对责任的误解或挪用等问题。人与人之间无缝协作背后的一个假设是，人类可以适应性地形成惯例。约定被定义为捕获交互并可以随时间变化的共享表示。然而，在人类-机器人团队中形成约定是困难的，因为人类伙伴是一个非固定的代理。在这个学院早期职业发展(Career)项目中，计划设计和测试适应性和基于约定的控制转移策略，以提高联合驾驶表现和对驾驶的主观评估。为此，本项目确定了两个研究目标。第一个目标侧重于学习人类和自动化系统之间的不同形式的约定。将创建一个模块化结构，将特定于合作伙伴的约定从依赖任务的表示中分离出来，并使用基于贝叶斯的优化方法学习不同的形式。此外，还将描述从约定空间到人机协作成果的映射。第二个目标是使用多目标贝叶斯优化为自动化系统开发算法，以便学习复杂的交互策略，并实现人与自动化系统之间的理想约定。该平台的有效性将通过一系列案例研究来验证，参与者使用触觉方向盘驾驶模拟器和地面车辆参与环路。这项职业拨款的总体研究目标是进一步促进人类和机器人团队之间的协作伙伴关系。鉴于人类和机器人都会受到故障的影响，移交问题-如何在人类和机器人之间交换控制权-在确保人-机器人团队的性能方面发挥着关键作用。然而，在触觉共享控制中平衡驾驶员的偏好和联合任务的安全性可能会导致几种可能的移交策略。虽然人类通过共同适应彼此来无缝地解决冲突，但人类和机器人之间的共同适应是相当具有挑战性的。该项目旨在开发触觉共享控制框架中的动态共同适应原理，在该框架中，人类驾驶员和自动化系统共同控制半自动地面车辆的转向。教育目标是通过几项活动为学生和未来的劳动力提供设计下一代人机系统的技术知识，包括整合研究和教学活动，并促进针对少数族裔学生和K-12学生的STEM教育。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Humans may gravitate to different strategies for resolving a conflict. However, current solutions for control transfer in semi-automated vehicles are mainly designed based on predefined rules and do not personalize the automation's strategies for resolving a conflict. As a result, these solutions face issues such as prolonged transfer time and misinterpretations or misappropriations of responsibility. A hypothesis behind the seamless human-human collaboration is that humans can adaptively form conventions. A convention is defined as shared representations that capture the interaction and can change over time. However, forming conventions in humans-robots teams is difficult because the human partner is a non-stationary agent. In this Faculty Early Career Development (CAREER) project, the plan is to design and test adaptable and convention-based control transfer strategies to enhance joint driving performance and subjective assessment of driving. To this end, two research objectives are defined for this project. The first objective focuses on learning different forms of conventions between humans and the automation system. A modular structure that separates partner-specific conventions from task-dependent representations will be created and used to learn different forms using Bayesian-based optimization approaches. Furthermore, a map from the space of conventions to outcomes in human-machine collaboration will be characterized. The second objective focuses on developing algorithms for automation systems using multi-objective Bayesian optimization so that complex interaction policies can be learned and a desirable convention between a human and an automation system can be achieved. The effectiveness of the platform will be validated through a series of case studies with human-subject participants in the loop using a haptic steering wheel driving simulator and a ground vehicle.The overarching research objective of this CAREER grant is to further enable collaborative partnerships between teams of humans and robots. Given that both humans and robots are subject to faults, the hand-off problem – how to exchange control between a human and robot— plays a critical role in ensuring the performance of a human-robot teaming. However, balancing the driver's preference and the joint task's safety in a haptic shared control may result in several possible handover strategies. While humans seamlessly resolve conflicts by co-adapting to each other, co-adaptation between humans and robots is quite challenging. This project aims to develop the principles of dynamic co-adaptation in a haptic shared control framework wherein both a human driver and an automation system collaboratively control a semi-automated ground vehicle's steering. The educational goal is to equip students and the future workforce with the technical knowledge for designing the next generation of human-machine systems through several activities, including integrating research and teaching activities and promoting STEM education for minority students and K-12 students.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.