用语音助手替代专家，实现个性化自动打包。
Pack It My Way: Triadic Human-Robot Collaboration for Personalized Autonomous Packing
- 设计三者协作框架，用语音代理代替人类专家进行偏好修正。
- 语音代理在两项偏好任务中表现接近人类专家，指令更短但效果相当。
- 适合希望降低人工干预的智能家居与自动化包装场景。
个性化自主打包需考虑居住者偏好，而这些偏好无法仅从场景几何推断。虽然专家远程操作员能理解偏好并转化为可行机器人动作，但持续介入限制了可扩展部署。本文研究居民、校正中介与机器人之间的三方协作，对比人类专家与语音代理作为中介的效果。通过在保护、紧凑性和分组任务中的用户研究，采用‘展示-纠正-泛化’流程评估偏好修正及周围物体重新排列后的泛化能力。结果表明，语音代理在三项偏好中的两项上达到与人类专家相当的表现，尽管其接收的指令更短且细节更少。两种中介使用难度相似，但人类专家被认为更可靠。研究证明语音代理有望减少专家参与，但仍面临感知可靠性与偏好泛化挑战。
原文摘要 · Abstract (English)
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.
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