Robot Behavior Personalization from Sparse User Feedback

TL;DR

This paper introduces TAACo, a framework enabling service robots to personalize behavior across diverse household tasks using minimal user feedback, by reasoning through abstract concepts for better generalization and explainability.

Contribution

The paper proposes a task adaptation method that generalizes personalization to new tasks with limited feedback, using abstract concepts for reasoning and explanation.

Findings

TAACo outperforms GPT-4 by 16% in prediction accuracy.

TAACo surpasses rule-based systems by 54%.

Effective with only 40 user feedback samples.

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 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.