NRI: Collaborative Research: Scalable Robot Autonomy through Remote Operator Assistance and Lifelong Learning

NRI：协作研究：通过远程操作员协助和终身学习实现可扩展的机器人自主性

• 批准号: 1637562
• 负责人: Sonia Chernova
• 金额: $ 26.62万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1637562&HistoricalAwards=false
• 关键词: NRI; Collaborative; Research; Scalable; Robot

One of the most significant barriers to the wider adoption of autonomous robotic systems in commercial applications is the challenge of achieving 100% reliable autonomy in unconstrained human environments. One path toward more robust autonomy is to spend more time in research labs improving robot capabilities, delaying deployment until autonomy is entirely robust. Instead, it may be valuable to deploy robots out in the wild and adapt their behavior based on the rare examples, corner cases, and contingencies encountered after deployment in order to achieve near-term, fully reliable autonomy. This approach is specifically motivated by the call center model, in which robots are deployed at end-user sites and contact a remote human operator for assistance whenever an error is encountered. This project develops a system that enables robots to perform lifelong, incremental improvement from remote human assistance with the long-term goal of achieving full autonomy. This research program has significant broader impacts, making personal robots more accessible to everyday people, while also providing opportunities for human-robot interaction that are ideal for educational K-12 programs, as well as undergraduate and graduate education. Towards these goals, novel algorithms, interfaces, and user studies are being developed to advance the state of the art in three key areas related to the call center model: (1) Robust, Multi-Sensory Task Outcome Detection: multimodal techniques for identifying conditions under which to seek assistance or deploy recovery behaviors; (2) Transparency Devices for Situated Awareness: visual and language interface modalities for increasing the situational awareness of the remote operator and allowing for intuitive interaction, leading to more efficient and correct recovery procedures; (3) Low-Level and High-Level Task Model Refinement: lifelong learning techniques for incorporating corrections and recovery procedures into existing task models, as well as active learning methods to collect more targeted data. The proposed approach is being evaluated on a variety of mobile manipulation tasks that a hotel concierge robot might perform, such as delivery tasks or preparing for and cleaning up after a conference banquet.

在商业应用中广泛采用自主机器人系统的最大障碍之一是在不受约束的人类环境中实现100%可靠自主的挑战。实现更强大的自主性的一条途径是在研究实验室中花费更多时间来提高机器人的能力，推迟部署，直到自主性完全强大。 相反，将机器人部署到野外并根据部署后遇到的罕见示例、极端情况和意外情况调整其行为，以实现短期、完全可靠的自主性，这可能是有价值的。这种方法特别受到呼叫中心模型的启发，在呼叫中心模型中，机器人部署在最终用户站点，并在遇到错误时联系远程操作员寻求帮助。 该项目开发了一个系统，使机器人能够从远程人类协助中进行终身渐进式改进，长期目标是实现完全自主。这项研究计划具有更广泛的影响，使个人机器人更容易接近日常生活，同时也为教育K-12计划以及本科和研究生教育提供了理想的人机交互机会。为了实现这些目标，正在开发新的算法、界面和用户研究，以推进与呼叫中心模型相关的三个关键领域的最新技术水平：（1）鲁棒的多感官任务结果检测：用于识别寻求帮助或部署恢复行为的条件的多模态技术;（2）用于情境感知的透明设备：视觉和语言界面模式，用于增加远程操作员的态势感知，并允许直观的交互，从而导致更有效和正确的恢复程序;（3）低级和高级任务模型细化：将纠正和恢复程序纳入现有任务模式的终身学习技术，以及收集更有针对性的数据的主动学习方法。 该方法正在评估酒店礼宾机器人可能执行的各种移动的操作任务，例如交付任务或会议宴会后的准备和清理。

项目成果

Towards Robust Skill Generalization: Unifying Learning from Demonstration and Motion Planning

• 发表时间: 2017-10
• 作者: M. A. Rana;Mustafa Mukadam;S. Ahmadzadeh;S. Chernova;Byron Boots

Active Learning within Constrained Environments through Imitation of an Expert Questioner

通过模仿专家提问者在受限环境中进行主动学习

• DOI: 10.24963/ijcai.2019/283
• 发表时间: 2019
• 期刊: Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence
• 作者: Bullard, Kalesha;Schroecker, Yannick;Chernova, Sonia
• 通讯作者: Chernova, Sonia

Learning Reactive Motion Policies in Multiple Task Spaces from Human Demonstrations

• 发表时间: 2019
• 作者: M. A. Rana;Anqi Li;H. Ravichandar;Mustafa Mukadam;S. Chernova;D. Fox;Byron Boots;Nathan D. Ratliff

Skill Acquisition via Automated Multi-Coordinate Cost Balancing

通过自动多坐标成本平衡获取技能

• DOI: 10.1109/icra.2019.8793762
• 发表时间: 2019
• 期刊: International Conference on Robotics and Automation
• 作者: Ravichandar, Harish;Ahmadzadeh, S. Reza;Rana, M. Asif;Chernova, Sonia
• 通讯作者: Chernova, Sonia

Learning Generalizable Robot Skills from Demonstrations in Cluttered Environments

• DOI: 10.1109/iros.2018.8593624
• 发表时间: 2018-08
• 期刊: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
• 作者: M. A. Rana;Mustafa Mukadam;S. Ahmadzadeh;S. Chernova;Byron Boots