S&AS: FND: Reliable Semi-Autonomy with Diminishing Reliance on Humans

建立可靠的自主系统，在没有人为干预的情况下构建和执行计划，以实现某些指定的目标，这一直是人工智能和机器人技术的标志。可靠的自主性正变得越来越重要，因为它使交通、健康和可持续生活等领域的创新应用成为可能。尽管取得了重大进展，但在自动驾驶汽车或移动服务机器人等全自动系统的长期、大规模部署方面仍存在相当大的障碍。这些障碍包括从技术和经济限制到道德和法律问题。该项目提供了一种全面的方法，通过建立依赖于丰富形式的人类援助的半自主系统来规避这些障碍，从建议到对系统的持续监督，以及接管控制权的可能性。该项目开发技术，以确保这些系统在人力援助延迟时的安全，并随着时间的推移减少对人力援助的依赖。此外，该项目还有助于在这一跨学科领域培训本科生和研究生，指导学生，特别关注代表性不足的群体，向当地学校开展外展活动，并加强工业合作。该项目回答了有关计划和学习算法的可行性、效率和可扩展性的基本问题，以支持半自治系统。该项目的主要目标是：(1)开发能够将自主权委托给具有某些限制的系统的技术，并提供强有力的保证，确保这些限制将得到尊重，并且即使在人工援助延迟时，系统也将保持安全状态；(2)开发规划和学习算法，认识到丰富形式的人类援助的可用性，并能有效地将这种辅助行动纳入总体计划；(3)通过建立分层多目标决策模型，解决了在不确定性和部分可观察性条件下优化人机交互的高计算复杂度问题；(4)利用人力协助，在新领域实现强大而准确的绘图和导航，同时随着时间的推移减少对人力监督的依赖。该项目在复杂的现实环境中评估了这些能力，包括校园规模的机器人部署、驾驶模拟器以及与日产合作的自动驾驶汽车。

Building reliable autonomous systems that can construct and execute plans to achieve some assigned goals, without human intervention, has been the hallmark of artificial intelligence and robotics since their inception. Reliable autonomy is becoming increasingly important as it enables innovative new applications in areas such as transportation, health, and sustainable living. Despite substantial progress, there are still considerable barriers to the long-term, large scale deployment of fully autonomous systems such as self-driving cars or mobile service robots. These barriers range from technological and economic constraints to ethical and legal issues. This project offers a comprehensive approach to circumvent these barriers by building semi-autonomous systems that rely on rich forms of human assistance, ranging from advice to constant supervision of the system with the possibility of taking over control. The project develops techniques to assure the safety of such systems when human assistance is delayed and to reduce their reliance on human assistance over time. Additionally, the project contributes to training of undergraduate and graduate students in this interdisciplinary area, mentoring of students with special attention to underrepresented groups, outreach activities to local schools, and strengthening of industrial collaborations.The project answers fundamental questions about the feasibility, efficiency, and scalability of planning and learning algorithms to support semi-autonomous systems. The main thrusts of the project are (1) develop techniques that can delegate autonomy to a system with some restrictions, and provide strong guarantees that these restrictions will be respected and that the system will maintain a safe state even when human assistance is delayed; (2) develop planning and learning algorithms that are cognizant of the availability of rich forms of human assistance and can effectively factor such assistive actions into the overall plan; (3) handle the high computational complexity of optimizing the interaction with humans under uncertainty and partial observability by creating a hierarchical multi-objective decision model; and (4) leverage human assistance to enable robust and accurate mapping and navigation in new areas, while reducing the reliance on human supervision over time. The project evaluates these capabilities in complex realistic settings involving a campus-scale robot deployment, a driving simulator, and autonomous vehicles in collaboration with Nissan.