An official website of the United States government
Large-language models (LLMs) hold significant promise in improving human-robot interaction, offering advanced conversational skills and versatility in managing diverse, open-ended user requests in various tasks and domains. Despite the potential to transform human-robot interaction, very little is known about the distinctive design requirements for utilizing LLMs in robots, which may differ from text and voice interaction and vary by task and context. To better understand these requirements, we conducted a user study (n = 32) comparing an LLM-powered social robot against text- and voice-based agents, analyzing task-based requirements in conversational tasks, including choose, generate, execute, and negotiate. Our findings show that LLM-powered robots elevate expectations for sophisticated non-verbal cues and excel in connection-building and deliberation, but fall short in logical communication and may induce anxiety. We provide design implications both for robots integrating LLMs and for fine-tuning LLMs for use with robots.
more »
« less
GPT-4 as a Moral Reasoner for Robot Command Rejection
To support positive, ethical human-robot interactions, robots need to be able to respond to unexpected situations in which societal norms are violated, including rejecting unethical commands. Implementing robust communication for robots is inherently difficult due to the variability of context in real-world settings and the risks of unintended influence during robots’ communication. HRI researchers have begun exploring the potential use of LLMs as a solution for language-based communication, which will require an in-depth understanding and evaluation of LLM applications in different contexts. In this work, we explore how an existing LLM responds to and reasons about a set of norm-violating requests in HRI contexts. We ask human participants to assess the performance of a hypothetical GPT-4-based robot on moral reasoning and explanatory language selection as it compares to human intuitions. Our findings suggest that while GPT-4 performs well at identifying norm violation requests and suggesting non-compliant responses, its flaws in not matching the linguistic preferences and context sensitivity of humans prevent it from being a comprehensive solution for moral communication between humans and robots. Based on our results, we provide a four-point recommendation for the community in incorporating LLMs into HRI systems.
more »
« less
- PAR ID:
- 10578929
- Publisher / Repository:
- Proceedings of the 12th International Conference on Human-Agent Interaction
- Date Published:
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
null (Ed.)Because robots are perceived as moral agents, they hold significant persuasive power over humans. It is thus crucial for robots to behave in accordance with human systems of morality and to use effective strategies for human-robot moral communication. In this work, we evaluate two moral communication strategies: a norm-based strategy grounded in deontological ethics, and a role-based strategy grounded in role ethics, in order to test the effectiveness of these two strategies in encouraging compliance with norms grounded in role expectations. Our results suggest two major findings: (1) reflective exercises may increase the efficacy of role-based moral language and (2) opportunities for moral practice following robots’ use of moral language may facilitate role-centered moral cultivation.more » « less
-
Empirical studies have suggested that language-capable robots have the persuasive power to shape the shared moral norms based on how they respond to human norm violations. This persuasive power presents cause for concern, but also the opportunity to persuade humans to cultivate their own moral development. We argue that a truly socially integrated and morally competent robot must be willing to communicate its objection to humans’ proposed violations of shared norms by using strategies such as blame-laden rebukes, even if doing so may violate other standing norms, such as politeness. By drawing on Confucian ethics, we argue that a robot’s ability to employ blame-laden moral rebukes to respond to unethical human requests is crucial for cultivating a flourishing “moral ecology” of human–robot interaction. Such positive moral ecology allows human teammates to develop their own moral reflection skills and grow their own virtues. Furthermore, this ability can and should be considered as one criterion for assessing artificial moral agency. Finally, this paper discusses potential implications of the Confucian theories for designing socially integrated and morally competent robots.more » « less
-
Human-Robot Collaboration (HRC) aims to create environments where robots can understand workspace dynamics and actively assist humans in operations, with the human intention recognition being fundamental to efficient and safe task fulfillment. Language-based control and communication is a natural and convenient way to convey human intentions. However, traditional language models require instructions to be articulated following a rigid, predefined syntax, which can be unnatural, inefficient, and prone to errors. This paper investigates the reasoning abilities that emerged from the recent advancement of Large Language Models (LLMs) to overcome these limitations, allowing for human instructions to be used to enhance human-robot communication. For this purpose, a generic GPT 3.5 model has been fine-tuned to interpret and translate varied human instructions into essential attributes, such as task relevancy and tools and/or parts required for the task. These attributes are then fused with perceived on-going robot action to generate a sequence of relevant actions. The developed technique is evaluated in a case study where robots initially misinterpreted human actions and picked up wrong tools and parts for assembly. It is shown that the fine-tuned LLM can effectively identify corrective actions across a diverse range of instructional human inputs, thereby enhancing the robustness of human-robot collaborative assembly for smart manufacturing.more » « less
-
Because robots are perceived as moral agents, they must behave in accordance with human systems of morality. This responsibility is especially acute for language-capable robots because moral communication is a method for building moral ecosystems. Language capable robots must not only make sure that what they say adheres to moral norms; they must also actively engage in moral communication to regulate and encourage human compliance with those norms. In this work, we describe four experiments (total N =316) across which we systematically evaluate two different moral communication strategies that robots could use to influence human behavior: a norm-based strategy grounded in deontological ethics, and a role-based strategy grounded in role ethics. Specifically, we assess the effectiveness of robots that use these two strategies to encourage human compliance with norms grounded in expectations of behavior associated with certain social roles. Our results suggest two major findings, demonstrating the importance of moral reflection and moral practice for effective moral communication: First, opportunities for reflection on ethical principles may increase the efficacy of robots’ role-based moral language; and second, following robots’ moral language with opportunities for moral practice may facilitate role-based moral cultivation.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
