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

机器人正越来越多地应用于社会的许多部门，包括教育、老年人护理、搜索和救援以及空间机器人技术。在所有这些领域，至关重要的是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。当通过自然语言进行命令时，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，如果机器人的指挥方式在遵守这些社会和道德规范的同时无法完全实现，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，并在可接受的情况下提供可接受的替代方案。虽然之前已经有一些关于伦理规划和命令拒绝的工作，但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。这项研究将开发一种智能物理系统，它能够(1)使用一组随机器人上下文变化的动态规范来执行伦理推理，(2)使用这些推理能力来有效地拒绝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规范集相关的上下文的丰富表示。当机器人进入现实世界时，这些能力是至关重要的，在现实世界中，它们(1)可能因为渎职或无知而被给予不道德的命令，(2)可能被要求在不是一个环境中操作，而是各种环境，每个环境可能都有自己相关的社会和道德规范，以及(3)可能需要从人类的指导和自己的感知中学习新的环境。考虑到这些现实世界的挑战，为了开发这个IPS，这项研究将产生第一个算法，用于在不确定、动态和现实感知的环境中识别和拒绝不适当的命令，使用以下技术：(1)不确定和开放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进行任务和运动规划；以及(3)用于长期自主和同时定位和映射的表征学习。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.