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.

在商业应用中更广泛采用自主机器人系统的最重要障碍之一是在不受约束的人类环境中获得100％可靠的自主权的挑战。通往更强大的自主权的一种途径是在研究实验室中花费更多的时间来提高机器人能力，将部署延迟，直到自主权完全稳健。 取而代之的是，在野外部署机器人并根据罕见的示例，角落案例和部署后遇到的意外情况来调整其行为可能很有价值，以实现近期，完全可靠的自主权。这种方法是由呼叫中心模型专门激励的，呼叫中心模型在最终用户站点部署机器人，并在遇到错误时与远程人类操作员联系以寻求帮助。 该项目开发了一个系统，该系统使机器人能够从远程人类援助中执行终身，增量的改进，并实现完全自治的长期目标。该研究计划具有更广泛的影响，使个人机器人更容易被日常的人访问，同时也为人类机器人互动提供了机会，这是教育K-12计划以及本科和研究生教育的理想选择。为了这些目标，正在开发新的算法，接口和用户研究，以推动与呼叫中心模型有关的三个关键领域的最新技术：（1）强大的多态任务结果检测：用于识别寻求帮助或部署恢复行为的条件的多模式技术； （2）位置意识的透明设备：可观的视觉和语言接口方式，用于提高远程操作员的情境意识并允许直观互动，从而实现更有效，更正确的恢复过程； （3）低级和高级任务模型的改进：将校正和恢复程序纳入现有任务模型的终身学习技术，以及积极的学习方法，以收集更多目标数据。 拟议的方法正在对酒店礼宾机器人可能执行的各种移动操纵任务进行评估，例如交货任务或准备会议宴会后准备和清理。

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

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

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