CAREER: Robust Perception and Customization for Long-Term Autonomous Mobile Service Robots

这个教师早期职业发展（CAREER）奖将使移动的服务机器人能够在现实世界的人类环境中运行很长一段时间。机器人感知的现有方法非常擅长推理世界的当前状态，但在推理随着时间的推移不可避免地发生的潜在变化方面存在明显的局限性。机器人感知的另一个常见问题是，当部署在不可预见的环境中时，机器人通常会由于不可预见的条件和违反设计假设而经历感知故障。最后，在操作使用期间的最终用户定制和增强是乏味和脆弱的。该项目将通过开发强大的算法方法来克服这些挑战，以识别和应对环境中的动态变化，识别感知故障并从中学习，并在操作中学习新任务。该研究将使移动的服务机器人能够在家庭、工作场所、灾区、医院和无数其他环境中开发和长期部署。作为项目的一部分，教育和推广计划将包括在整个学年中对本科生进行教育和指导的纵向努力，以及为初中到高中学生举办的计算机研讨会和有趣的机器人活动。该项目的目标是开发强大的算法公式以及分析和符号模型，以实现长期的服务机器人在动态人类环境中的持续自主移动的操作。首先，将介绍机器人感知的重新表述，该重新表述将明确地推理世界的当前状态与随着时间的推移可能发生的变化之间的关系，即物体可能在世界上表现出的几何形状、视觉外观和运动类型。其次，将开发方法，使机器人能够通过利用冗余感知和感知预测与实际结果之间的差异来自主构建其感知能力的模型，从而使它们能够避免或克服未来可能导致错误的情况。最后，将开发技术，以解决可定制性和使用物理启发的符号程序的新任务的学习。开发的方法将在多个集成级别上进行严格测试，包括部署在室内和室外的自主移动的服务机器人团队，执行包括包裹递送，导游图尔斯和环境监测在内的任务。该项目得到了跨部门机器人基础研究计划的支持，由工程局（ENG）和计算机与信息科学与工程局（CISE）共同管理和资助该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

This Faculty Early Career Development (CAREER) award will enable mobile service robots capable of operating in real-world human environments over extended periods of time. Existing approaches in robot perception are very good at reasoning about the current state of the world but suffer from a marked limitation in reasoning about potential changes that inevitably occur over time. Another common problem of robot perception is that when deployed in unforeseen environments, robots commonly experience perception failures due to unanticipated conditions and violations of design assumptions. Finally, end-use customization and enhancements during operational use is tedious and fragile. This project will overcome these challenges by developing robust algorithmic approaches to recognize and react to dynamic changes in environment, identify failures in perception and learn from them, and additionally learn new tasks while in operation. The research will enable the development and long-term deployment of mobile service robots in homes, workplaces, disaster zones, hospitals, and myriad other environments. As part of the project, the education and outreach plan will include a longitudinal effort for the education and mentoring of undergraduate students throughout the academic year as well as computing workshops with fun robotic activities for middle to high school students.This objective of this project is to develop robust algorithmic formulations and analytical and symbolic models to enable long-duration autonomous mobile operations of service robots in dynamic human environments. First, a reformulation of robot perception will be introduced that will explicitly reason about the relation between the current state of the world and possible changes over time, in terms of the geometric shapes, visual appearances, and types of motions that objects are likely to exhibit in the world. Second, approaches will be developed for robots to autonomously build models of their perception competence by leveraging redundant sensing and discrepancies between perceptual predictions and actual outcomes, thus enabling them to avoid or overcome future situations that would lead to errors. Finally, techniques will be developed to address customizability and learning of novel tasks using physics-inspired symbolic programs. The approaches developed will be rigorously tested at multiple levels of integration, including on a team of autonomous mobile service robots deployed indoors and outdoors, performing tasks including package delivery, guided tours, and environment monitoring.This project is supported by the cross-directorate Foundational Research in Robotics program, jointly managed and funded by the Directorates for Engineering (ENG) and Computer and Information Science and Engineering (CISE).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.