发表机构
New Jersey Institute of Technology（新泽西理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文研究三元人机协作，对比人类专家与语音代理作为中介的个性化自主打包，发现语音代理在两类偏好任务中表现相当，可减少专家参与，但存在可靠性感知与偏好泛化挑战。
AI 中文摘要
个性化自主打包要求机器人考虑仅从场景几何无法推断的居住者偏好。专家远程操作者可解读这些偏好并将其转化为可行的机器人动作，但持续的专家参与限制了可扩展部署。本文中，我们研究由居住者、修正中介和机器人组成的三元人机协作，对比人类专家中介与语音代理中介两种情况。我们在包含保护、紧凑性和分组任务的用户研究中评估这两种条件，采用“展示-修正-泛化”流程评估偏好修正及周围物体重新排列后的后续泛化能力。结果显示，尽管语音代理接收的指令更短、细节更少，但在三类偏好中的两类上，语音代理中介取得了与人类专家中介相当的结果。两种中介的易用性相似，不过人类专家被认为更可靠。这些发现表明，语音代理有减少专家参与的潜力，同时指出感知可靠性和偏好泛化是仍需解决的挑战。
英文摘要
Personalized autonomous packing requires robots to account for resident preferences that cannot be inferred from scene geometry alone. Expert teleoperators can interpret these preferences and translate them into feasible robot actions, but continuous expert involvement limits scalable deployment. In this paper, we investigate triadic human-robot collaboration among a resident, a correction mediator, and a robot by comparing human-expert and voice-agent mediation. We evaluate the two conditions in a user study across Protection, Compactness, andGrouping tasks, using a Show-Correct-Generalize process to assess preference correction and subsequent generalization after the surrounding objects are rearranged. Results show that voice-agent mediation achieves outcomes comparable to human-expert mediation in two of the three preference categories, despite receiving shorter and less detailed instructions. Both mediators are similarly easy to use, although the human expert is perceived as more reliable. These findings demonstrate the potential of voice agents to reduce expert involvement while identifying perceived reliability and preference generalization as remaining challenges.
