RI: Medium: Computational Models, Interaction Mechanisms, and Planning Algorithms for Semi-Autonomous Systems

自主系统对社会产生了变革性的影响，因为它们有助于减少人力，降低风险和成本，并提高生产率和效率。它们已经部署在从家用产品到空间探索飞行器的广泛领域。然而，在许多领域，部署完全自主的系统仍然存在相当大的障碍。这些障碍从技术问题到道德和法律问题都有。例如，驾驶汽车、在搜救行动中部署机器人、自动化农业和机器人手术。当完全自动化不可行时，通常希望将整个过程的某些部分自动化。这个项目对半自主系统的规划进行了全面的研究--这种系统在某些条件下能够自主运行，但可能需要人工控制才能完成手头的任务。半自动系统的规划具有挑战性，因为它必须考虑到操作员和自动化系统的不同技能，它们之间促进控制顺利转移所需的沟通，关于人类反应能力、参与程度和接管控制准备情况的不确定性，以及在解释或遵循计划时可能出现的人为错误。该项目采用了跨学科的方法，解决了计算方面的挑战，以及每当人类处于循环中时出现的挑战。研究活动以半自动驾驶为主要领域，包括：设计通用图形模型来表示半自动系统的协同控制问题；开发有效的方法来表示和获取参与者的能力模型；开发高效的决策理论规划算法，利用启发式搜索和可达性分析来创建共享计划；开发算法来计算关于共享计划的重要统计数据和运行时反馈；开发方法来捕获态势感知和人为错误的模型，并将其纳入规划过程；以及为半自主系统中的规划创建一组具有挑战性的场景和测试问题。使用包括两个逼真驾驶模拟器在内的多个试验台对该方法进行了评估。

Autonomous systems offer transformational impacts on society as they help reduce human labor, decrease risks and costs, and improve productivity and efficiency. They have been deployed in a wide range of domains from household products to space exploration vehicles. In many areas, however, there are still considerable barriers to the deployment of fully autonomous systems. These barriers range from technological to ethical and legal issues. Examples include driving a car, robot deployment in search and rescue operations, automated farming, and robotic surgery. When full autonomy is not feasible, it is often desirable to automate parts of the entire process. This project offers a comprehensive study of planning for semi-autonomous systems -- systems that are capable of autonomous operation under some conditions, but may require manual control in order to complete the task at hand. Planning for semi-autonomous systems is challenging because it must account for the different skills of the human operator and the automated system, the communication between them required to facilitate smooth transfer of control, the uncertainty about human responsiveness, engagement level and readiness to take over control, and the possibility of human error in interpreting or following the plan. The project takes an interdisciplinary approach that addresses the computational challenges together with the challenges that rise whenever the human is in the loop. With a focus on semi-autonomous driving as the primary domain, research activities include: designing general-purpose graphical models to represent the problem of collaborative control of semi-autonomous systems; developing effective methods to represent and earn competence models of the actors; developing efficient decision-theoretic planning algorithms that exploit heuristic search and reachability analysis to create the shared plan; developing algorithms to compute vital statistics and runtime feedback about the shared plan; developing ways to capture models of situation awareness and human errors, and factor them into the planning process; and creating a set of challenging scenarios and test problems for planning in semi-autonomous systems. Evaluation of the approach is conducted using several testbeds including two realistic driving simulators.