EAGER: Hierarchical Contrastive Explanations for Robot-Human Communication

EAGER：机器人与人类交流的分层对比解释

• 批准号: 1844325
• 负责人: Siddharth Srivastava
• 金额: $ 27.46万
• 依托单位: Arizona State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2021-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1844325&HistoricalAwards=false
• 关键词: EAGER; Hierarchical; Contrastive; Explanations; Robot

Intelligent assistive robots have the potential to improve our society in many walks of life: they could help us take care of the elderly and the sick, act as first responders in emergencies, and kickstart extra-planetary exploration. However, today's robots require highly trained experts for their customization, configuration, and repair. This not only makes it difficult to realize the potential benefits of assistive robots in society, but also creates large uncertainties in the future of employment for millions in the workforce. To help address such issues, this project develops new ways for robots to explain their actions to humans, considering the proficiency of the users. Thus, robots will be able to tailor their explanations to what someone already understands about the robots' capabilities and limitations.The project focuses on automatically explaining unexpected robot behavior to users with potentially imprecise knowledge about the underlying task and/or the robot. This can be used to efficiently diagnose specification problems and customize robots towards desired behaviors. The proposed approach formalizes three general principles: 1) customizing explanations according to the audience; 2) treating explanation as an interactive process; and 3) using the questions asked of a robot to estimate the user's level of expertise. In this framework, a user may present a foil, or a counterfactual proposal of alternative robot behavior, that s/he finds more natural. The proposed approach estimates the user's proficiency using a lattice of abstract models and computes reasons why the proposed alternatives would not work using minimal additional detail. In this way, the system can produce explanations that are contrastive and aligned with the proficiency of the user.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.

智能辅助机器人有潜力在各行各业改善我们的社会：它们可以帮助我们照顾老人和病人，在紧急情况下充当第一反应者，并启动外行星探索。 然而，今天的机器人需要训练有素的专家进行定制，配置和维修。这不仅使辅助机器人在社会中的潜在利益难以实现，而且还为数百万劳动力的就业前景带来了巨大的不确定性。 为了帮助解决这些问题，该项目开发了机器人向人类解释其行为的新方法，同时考虑到用户的熟练程度。 因此，机器人将能够根据用户对机器人的能力和局限性的了解来定制他们的解释。该项目的重点是自动向用户解释意外的机器人行为，这些用户对潜在的任务和/或机器人的知识可能不精确。这可以用来有效地诊断规范问题，并根据所需的行为定制机器人。所提出的方法形式化的三个一般原则：1）根据观众定制的解释; 2）治疗解释作为一个互动的过程;和3）使用的机器人提出的问题，以估计用户的专业知识水平。在这个框架中，用户可以提出一个陪衬，或替代机器人行为的反事实建议，他/她觉得更自然。所提出的方法估计用户的熟练程度使用一个抽象的模型格和计算的原因，为什么建议的替代品不会使用最少的额外的细节。通过这种方式，该系统可以产生对比性的解释，并与用户的熟练程度保持一致。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。

项目成果

Why Can't You Do That HAL? Explaining Unsolvability of Planning Tasks

为什么你不能这样做 HAL？

• 发表时间: 2019
• 期刊: International Joint Conference on Artificial Intelligence
• 作者: Sreedharan, Sarath;Srivastava, Siddharth;Smith, David;Kambhampati, Subbarao.
• 通讯作者: Kambhampati, Subbarao.

Asking the Right Questions: Learning Interpretable Action Models Through Query Answering

• DOI: 10.1609/aaai.v35i13.17428
• 发表时间: 2021-05
• 作者: Pulkit Verma;Shashank Rao Marpally;Siddharth Srivastava

TLdR: Policy Summarization for Factored SSP Problems Using Temporal Abstractions

TLdR：使用时间抽象对因子式 SSP 问题进行策略总结

• 发表时间: 2020
• 期刊: Proceedings of the International Conference on Automated Planning and Scheduling
• 作者: Sarath Sreedharan, Siddharth Srivastava