用少量用户反馈让机器人学会个性化行为，还能解释原因。
Robot Behavior Personalization from Sparse User Feedback
- 通过抽象概念表征用户反馈，学习任务适配函数。
- 仅40条反馈就使预测准确率比GPT-4高16%。
- 适合需要快速适应新用户的新家庭服务机器人。
随着服务机器人功能日益通用，它们需适应用户在各类任务中的偏好，包括哪些动作应由机器人执行。现有个性化方法需为每位用户收集特定任务数据。为应对家庭任务与用户间的多样性及偏好差异，我们提出独立学习任务适配函数的框架——基于抽象概念的任务适配（TAACo）。TAACo通过抽象概念表征用户反馈，推理并预测用户期望的协助方式，可泛化至开放集家庭任务，且能以直观概念解释推理过程。我们在5名用户的数据集上评估，仅用40条反馈样本，其预测准确率即比GPT-4高出16%，比规则系统高54%。
原文摘要 · Abstract (English)
As service robots become more general-purpose, they will need to adapt to their users' preferences over a large set of all possible tasks that they can perform. This includes preferences regarding which actions the users prefer to delegate to robots as opposed to doing themselves. Existing personalization approaches require task-specific data for each user. To handle diversity across all household tasks and users, and nuances in user preferences across tasks, we propose to learn a task adaptation function independently, which can be used in tandem with any universal robot policy to customize robot behavior. We create Task Adaptation using Abstract Concepts (TAACo) framework. TAACo can learn to predict the user's preferred manner of assistance with any given task, by mediating reasoning through a representation composed of abstract concepts built based on user feedback. TAACo can generalize to an open set of household tasks from small amount of user feedback and explain its inferences through intuitive concepts. We evaluate our model on a dataset we collected of 5 people's preferences, and show that TAACo outperforms GPT-4 by 16% and a rule-based system by 54%, on prediction accuracy, with 40 samples of user feedback.
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