NRI: Small: Collaborative Planning for Human-robot Science Teams

NRI：小型：人机科学团队的协作规划

• 批准号: 1317815
• 负责人: Gaurav Sukhatme
• 金额: $ 48.23万
• 依托单位: University of Southern California
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-10-01 至 2017-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1317815&HistoricalAwards=false
• 关键词: NRI; Small; Collaborative; Planning; Human

This project envisions the future of scientific exploration as a collaborative endeavor between human scientists and autonomous robotic systems. The key challenge to materializing this vision lies in combining the expert knowledge of the scientist with the optimization capabilities of the autonomous system. The scientist brings specialized knowledge and experience to the table, while the autonomous system is capable of processing and evaluating large quantities of data. This research leverages these complementary strengths to develop a collaborative system capable of guiding scientific exploration and data collection by integrating input from scientists into an autonomous learning and planning framework. This is achieved by combining probabilistic planning with inverse reinforcement learning to integrate human input and prior knowledge into a unified optimization framework in the context of scientific exploration. The project team is validating the approach in the challenging domain of autonomous underwater ocean monitoring. This domain is particularly well suited for the testing of human-robot collaboration due to the limited communication available underwater and the necessary supervised autonomy capabilities. By integrating feedback from the human user into an algorithmic planning framework, the goal is to improve the efficiency of scientific data collection and gather data about phenomena that were previously outside the reach of scientific investigation. The use of autonomous vehicles for scientific data collection is becoming increasingly prominent; however, the research community lacks a foundational understanding of the interactions between scientists and autonomous vehicles. This work focuses on principled methods for integrating human input into algorithmic optimization techniques moving towards the goal of supervised autonomy for robots.This project has the potential to change the way scientific data are collected through the development of a foundational framework for human-robot scientific collaboration. Such a framework is expected to have broad implications throughout the fields of human-robot interaction and artificial intelligence. The proposed research is being integrated into the robotics and computer science curriculum at both the graduate and undergraduate levels. It is also being utilized for K-12 robotics outreach programs in Los Angeles. The algorithms created in this research are transitioned to field tests and operations via ongoing collaborations with the Monterey Bay Aquarium Research Institute (MBARI) and the Southern California Coastal Ocean Observing System (SCCOOS).

该项目将科学探索的未来设想为人类科学家和自主机器人系统之间的合作奋进。实现这一愿景的关键挑战在于将科学家的专业知识与自主系统的优化能力相结合。科学家将专业知识和经验带到桌面上，而自主系统能够处理和评估大量数据。这项研究利用这些互补的优势，开发一个协作系统，能够通过将科学家的投入整合到自主学习和规划框架中，指导科学探索和数据收集。这是通过将概率规划与反向强化学习相结合来实现的，将人类输入和先验知识整合到科学探索背景下的统一优化框架中。该项目团队正在验证自主水下海洋监测这一具有挑战性的领域中的方法。由于水下通信有限和必要的监督自治能力，该领域特别适合于测试人机协作。通过将人类用户的反馈整合到算法规划框架中，目标是提高科学数据收集的效率，并收集有关以前科学调查范围之外的现象的数据。使用自动驾驶汽车进行科学数据收集正变得越来越突出;然而，研究界对科学家和自动驾驶汽车之间的相互作用缺乏基本的了解。该项目的重点是将人类输入整合到算法优化技术中的原则性方法，以实现机器人监督自治的目标。该项目有可能通过开发人类-机器人科学合作的基础框架来改变科学数据的收集方式。这样的框架有望在人机交互和人工智能领域产生广泛的影响。拟议中的研究正在被整合到研究生和本科生的机器人和计算机科学课程中。它也被用于洛杉矶的K-12机器人推广计划。通过与蒙特雷湾水族馆研究所（MBARI）和南加州沿海海洋观测系统（SCCOOS）的持续合作，将本研究中创建的算法过渡到现场测试和操作。