Robot Behavior Personalization From Sparse User Feedback

IF 4.6 2区计算机科学 Q2 ROBOTICS

IEEE Robotics and Automation Letters Pub Date : 2025-03-12 DOI:10.1109/LRA.2025.3550833

Maithili Patel;Sonia Chernova

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引用次数: 0

Abstract

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 personalize 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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根据稀疏用户反馈个性化机器人行为

随着服务机器人的通用性越来越强，它们需要适应用户对其所能执行的大量任务的偏好。这包括用户更愿意将哪些任务委托给机器人而不是自己完成。现有的个性化方法需要每个用户的特定任务数据。为了处理所有家庭任务和用户的多样性，以及不同任务中用户偏好的细微差别，我们建议独立学习任务适应函数，该函数可与任何通用机器人策略结合使用，以个性化机器人行为。我们创建了使用抽象概念的任务适应（TAACo）框架。TAACo 可通过基于用户反馈建立的抽象概念表征进行中介推理，从而学会预测用户对任何给定任务的首选协助方式。TAACo 可以从少量用户反馈中归纳出一组开放的家庭任务，并通过直观概念解释其推论。我们在一个收集了 5 个人偏好的数据集上对我们的模型进行了评估，结果表明，在 40 个用户反馈样本中，TAACo 的预测准确率比 GPT-4 高出 16%，比基于规则的系统高出 54%。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Robotics and Automation Letters Computer Science-Computer Science Applications

CiteScore

9.60

自引率

15.40%

发文量

1428

期刊介绍： The scope of this journal is to publish peer-reviewed articles that provide a timely and concise account of innovative research ideas and application results, reporting significant theoretical findings and application case studies in areas of robotics and automation.