S&AS: FND: Context-Aware Ethical Autonomy for Language Capable Robots

S

• 批准号: 1849348
• 负责人: Thomas Williams
• 金额: $ 57万
• 依托单位: Colorado School of Mines
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-02-15 至 2023-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1849348&HistoricalAwards=false
• 关键词: S&AS; FND; Context; Aware; Ethical

Robots are being increasingly used across many sectors of society, including education, eldercare, search and rescue, and space robotics. In all these domains, it is crucial that robots be able to accept commands through natural language, so as to enable natural, effective, and understandable interaction. When commanded through natural language, robots must be able to ensure that the way that they achieve users' commands aligns with human expectations, especially the social and moral rules humans agree to by community consensus, known as social and moral norms. Moreover, if a robot is commanded in a way that cannot be fully achieved while complying with these social and moral norms, that robot must be able to explain to the user why it must reject the given command, offering acceptable alternatives when available. While there has been some previous work on ethical planning and command rejection, it has largely not accounted for cases in which robots are uncertain about social or moral norms or cases in which social and moral norms change between contexts. Research is needed to give robots the perceptual capabilities they need to identify such contexts, as well as the rich language understanding and generation abilities needed to communicate about social and moral norms.This research will develop an Intelligent Physical System capable of (1) performing ethical reasoning using a dynamic set of norms that changes along with the robot's context, (2) using these reasoning capabilities to effectively reject or offer alternatives to inappropriate commands, and (3) learning rich representations of the contexts relevant to its set of moral and social norms. These capabilities are crucial as robots move into the real world, in which they (1) may be given unethical commands, either due to malfeasance or ignorance, (2) may be required to operate in not one context, but a variety of contexts, each which may have their own relevant social and moral norms, and (3) may need to learn about new contexts from both human instruction and their own perception. In order to develop this IPS in consideration of these real-world challenges, this research will produce the first algorithms for identifying and rejecting inappropriate commands in uncertain, dynamic, and realistically perceived contexts, using techniques form (1) natural language understanding and generation in uncertain and open worlds and Dempster-Shafer Theory; (2) task and motion planning through constrained inference and constrained optimization; and (3) representation learning for long-term autonomy and simultaneous localization and mapping.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.

机器人越来越多地应用于社会许多领域，包括教育、老年护理、搜索和救援以及太空机器人。在所有这些领域中，机器人能够通过自然语言接受命令，从而实现自然、有效和可理解的交互至关重要。当通过自然语言进行命令时，机器人必须能够确保其实现用户命令的方式符合人类的期望，尤其是人类通过社区共识同意的社会和道德规则，即社会和道德规范。此外，如果机器人所接受的命令在遵守这些社会和道德规范的同时无法完全实现，则该机器人必须能够向用户解释为什么它必须拒绝给定的命令，并在可用时提供可接受的替代方案。虽然之前有一些关于道德规划和命令拒绝的工作，但它在很大程度上没有考虑到机器人对社会或道德规范不确定的情况，或者社会和道德规范在不同背景下发生变化的情况。需要进行研究，赋予机器人识别此类环境所需的感知能力，以及交流社会和道德规范所需的丰富语言理解和生成能力。这项研究将开发一种智能物理系统，能够（1）执行道德规范使用随机器人环境而变化的动态规范集进行推理，（2）使用这些推理能力有效地拒绝不适当的命令或提供不适当命令的替代方案，以及（3）学习与其道德和社会环境相关的丰富环境表示规范。当机器人进入现实世界时，这些能力至关重要，在现实世界中，它们 (1) 可能会因为渎职或无知而收到不道德的命令，(2) 可能需要在不同的环境中操作，而是在多种环境中操作，每个都可能有自己相关的社会和道德规范，并且（3）可能需要从人类指导和自己的感知中了解新的环境。为了开发此 IPS 并考虑到这些现实世界的挑战，本研究将产生第一个算法，用于在不确定、动态和现实感知的上下文中识别和拒绝不适当的命令，使用形式 (1) 自然语言理解和生成技术不确定和开放的世界和登普斯特-谢弗理论； (2)通过约束推理和约束优化进行任务和运动规划； (3) 长期自治和同步定位与测绘的表征学习。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力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

“We Need to Start Thinking Ahead”: The Impact of Social Context on Linguistic Norm Adherence

“我们需要开始提前思考”：社会背景对语言规范遵守的影响

• 发表时间: 2020-01
• 期刊: Annual Meeting of the Cognitive Science Society
• 作者: Lockshin, Jane;Williams, Tom
• 通讯作者: Williams, Tom

Task and Motion Planning

任务和运动规划

• DOI: 10.1007/978-3-642-41610-1_176-1
• 发表时间: 2020-11
• 期刊: Springer Encyclopedia of Robotics
• 作者: Dantam; Neil T
• 通讯作者: Neil T

Visual Reference of Ambiguous Objects for Augmented Reality-Powered Human-Robot Communication in a Shared Workspace

共享工作空间中增强现实驱动的人机通信的模糊对象的视觉参考

• 发表时间: 2020-07
• 期刊: Augmented and Mixed Reality
• 作者: Gao, Peng;Reily, Brian;Paul, Savannah;Zhang, Hao
• 通讯作者: Zhang, Hao

Why and How Robots Should Say ‘No’

机器人为什么以及如何说“不”

• DOI: 10.1007/s12369-021-00780-y
• 发表时间: 2021-05-17
• 期刊: International Journal of Social Robotics
• 影响因子: 4.7
• 作者: Gordon Briggs;T. Williams;R. Jackson;Matthias Scheutz
• 通讯作者: Matthias Scheutz

Failure Explanation in Privacy-Sensitive Contexts: An Integrated Systems Approach

隐私敏感环境中的失败解释：集成系统方法

• 发表时间: 2023-01
• 期刊: IEEE International Conference on Robot and Human Interactive Communication (RO-MAN
• 作者: Li, Sihui;Siva, Sriram;Mott, Terran;Williams, Tom;Zhang, Hao;Dantam, Neil
• 通讯作者: Dantam, Neil