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

S

• 批准号: 1724101
• 负责人: Shlomo Zilberstein
• 金额: $ 69.95万
• 依托单位: University of Massachusetts Amherst
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2022-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1724101&HistoricalAwards=false
• 关键词: S&AS; FND; Reliable; Semi; Autonomy

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.

构建可靠的自主系统，可以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。 可靠的自动驾驶变得越来越重要，因为它可以在交通、健康和可持续生活等领域实现创新应用。 尽管取得了实质性进展，但长期、大规模部署全自动系统（如自动驾驶汽车或移动的服务机器人）仍存在相当大的障碍。 这些障碍从技术和经济限制到道德和法律的问题。 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的可能性。 该项目开发技术，以确保这些系统的安全时，人力援助被推迟，并减少其依赖人力援助随着时间的推移。 此外，该项目还有助于培养这一跨学科领域的本科生和研究生，特别关注代表性不足的群体，对当地学校开展外联活动，并加强产业合作。该项目回答了有关规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）开发技术，可以将自治权委托给具有某些限制的系统，并提供强有力的保证，这些限制将得到尊重，即使在人类援助延迟的情况下，系统也将保持安全状态;（二）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，并能有效地考虑这种辅助性该方法包括：（1）在不确定性和部分可观测性下，通过创建分层多目标决策模型，处理优化与人类交互的高计算复杂性;（2）利用人工辅助，在新区域实现稳健和准确的地图绘制和导航，同时随着时间的推移减少对人类监督的依赖。 该项目在复杂的现实环境中评估这些能力，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。

项目成果

Online Decision-Making for Scalable Autonomous Systems

可扩展自治系统的在线决策

• DOI: 10.24963/ijcai.2017/664
• 发表时间: 2017
• 期刊: International Joint Conference on Artificial Intelligence
• 作者: Wray, Kyle Hollins;Witwicki, Stefan J.;Zilberstein, Shlomo
• 通讯作者: Zilberstein, Shlomo