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

职业：长期自主移动服务机器人的鲁棒感知和定制

• 批准号: 2046955
• 负责人: Joydeep Biswas
• 金额: $ 59.05万
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-04-01 至 2026-03-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2046955&HistoricalAwards=false
• 关键词: CAREER; Robust; Perception; Customization; Long

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.

该学院早期职业发展（CAREER）奖将使移动服务机器人能够在现实世界的人类环境中长时间运行。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时受到明显的限制。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，机器人通常会由于意外条件和违反设计假设而出现感知失败。最后，操作使用期间的最终用途定制和增强是乏味且脆弱的。该项目将通过开发强大的算法方法来克服这些挑战，以识别环境的动态变化并做出反应，识别感知失败并从中学习，并在运行时学习新任务。该研究将使移动服务机器人在家庭、工作场所、灾区、医院和无数其他环境中的开发和长期部署成为可能。作为该项目的一部分，教育和推广计划将包括在整个学年对本科生进行纵向教育和指导，以及为中学生和高中生举办有趣的机器人活动的计算机研讨会。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，以实现服务机器人在动态人类环境中的长期自主移动操作。首先，将引入机器人感知的重新表述，它将根据物体可能表现出的几何形状、视觉外观和运动类型，明确推理世界当前状态与随时间可能发生的变化之间的关系在世界上。其次，将开发方法，让机器人通过利用冗余传感以及感知预测与实际结果之间的差异，自主构建其感知能力模型，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，将开发技术来使用受物理启发的符号程序来解决新任务的可定制性和学习问题。所开发的方法将在多个集成层面进行严格测试，包括部署在室内和室外的自主移动服务机器人团队，执行包裹递送、导游和环境监测等任务。该项目得到了跨部门基金会的支持机器人研究项目，由工程理事会 (ENG) 和计算机与信息科学与工程理事会 (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智力优势和更广泛的评估进行评估，认为值得支持影响审查标准。

项目成果

ObVi-SLAM: Long-Term Object-Visual SLAM

• DOI: 10.1109/lra.2024.3363534
• 发表时间: 2023-09
• 期刊: IEEE Robotics and Automation Letters
• 影响因子: 5.2
• 作者: Amanda Adkins;Taijing Chen;Joydeep Biswas

STEADY: Simultaneous State Estimation and Dynamics Learning from Indirect Observations

STEADY：从间接观察中同时进行状态估计和动力学学习

• DOI: 10.1109/iros47612.2022.9981279
• 发表时间: 2022
• 期刊: IEEE/RSJ International Conference on
• 作者: Wei, Jiayi;Holtz, Jarrett;Dillig, Isil;Biswas, Joydeep
• 通讯作者: Biswas, Joydeep

High-Speed Accurate Robot Control using Learned Forward Kinodynamics and Non-linear Least Squares Optimization

• DOI: 10.1109/iros47612.2022.9981259
• 发表时间: 2022-06
• 期刊: 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
• 作者: P. Atreya;Haresh Karnan;Kavan Singh Sikand;Xuesu Xiao;Garrett Warnell;Sadegh Rabiee;P. Stone;Joydeep Biswas

Robofleet: Open Source Communication and Management for Fleets of Autonomous Robots

Roofleet：自主机器人车队的开源通信和管理

• DOI: 10.1109/iros51168.2021.9635830
• 发表时间: 2021
• 期刊: 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS
• 作者: Sikand, Kavan Singh;Zartman, Logan;Rabiee, Sadegh;Biswas, Joydeep
• 通讯作者: Biswas, Joydeep

VI-IKD: High-Speed Accurate Off-Road Navigation using Learned Visual-Inertial Inverse Kinodynamics

• DOI: 10.1109/iros47612.2022.9982060
• 发表时间: 2022-03
• 期刊: 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
• 作者: Haresh Karnan;Kavan Singh Sikand;P. Atreya;Sadegh Rabiee;Xuesu Xiao;Garrett Warnell;P. Stone;Joydeep Biswas