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.

机器人越来越多地用于社会的许多领域，包括教育，老年人护理，搜索和救援以及空间机器人。在所有这些领域中，机器人能够通过自然语言接受命令至关重要，以便实现自然，有效和可理解的交互。当通过自然语言命令时，机器人必须能够确保它们实现用户命令的方式符合人类的期望，特别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，如果机器人被命令的方式在遵守这些社会和道德规范的情况下无法完全实现，那么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。虽然之前已经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环境之间变化的情况。需要研究赋予机器人识别这种环境所需的感知能力，以及丰富的语言理解和生成能力，以交流社会和道德规范。本研究将开发一种智能物理系统，该系统能够（1）使用动态规范集进行伦理推理，该规范集随机器人的环境沿着变化，（2）使用这些推理能力来有效地拒绝或提供替代不适当的命令，（3）学习与其道德和社会规范相关的背景的丰富表征。当机器人进入真实的世界时，这些能力是至关重要的，在这个世界中，它们（1）可能会因为渎职或无知而被给予不道德的命令，（2）可能需要在不止一个环境中操作，而是在各种环境中操作，每个环境都可能有自己相关的社会和道德规范，（3）可能需要从人类的指令和自己的感知中学习新的环境。为了在考虑这些现实世界的挑战的情况下开发这种IPS，本研究将产生用于识别和拒绝不确定的、动态的和现实感知的上下文中的不适当的命令的第一算法，使用以下技术：（1）在不确定的和开放的世界中的自然语言理解和生成以及Dempster-Shafer理论;（2）通过约束推理和约束优化的任务和运动规划;和（3）长期自主和同步本地化和映射的表征学习。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

Voxel-Based Representation Learning for Place Recognition Based on 3D Point Clouds

• DOI: 10.1109/iros45743.2020.9340992
• 发表时间: 2020-10
• 期刊: 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
• 作者: S. Siva;Zachary Nahman;Hao Zhang

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
• 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

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
• 期刊: Springer Encyclopedia of Robotics
• 作者: Dantam, Neil T

Long-term Place Recognition through Worst-case Graph Matching to Integrate Landmark Appearances and Spatial Relationships

• DOI: 10.1109/icra40945.2020.9196906
• 发表时间: 2020-05
• 期刊: 2020 IEEE International Conference on Robotics and Automation (ICRA)
• 作者: Peng Gao;Hao Zhang

Why and How Robots Should Say ‘No’

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