Predicting Routine Object Usage for Proactive Robot Assistance

TL;DR

This paper introduces SLaTe-PRO, a model that predicts object usage in household routines to enable proactive robot assistance, incorporating user history and interactive queries to handle stochastic behaviors.

Contribution

We propose SLaTe-PRO, a novel sequential model that combines object and user action data with an interactive query mechanism for improved routine prediction.

Findings

SLaTe-PRO achieves an F1 score of 0.57 without queries.

User queries increase the F1 score to 0.60.

The approach outperforms prior work with a baseline score of 0.43.

Abstract

Proactivity in robot assistance refers to the robot's ability to anticipate user needs and perform assistive actions without explicit requests. This requires understanding user routines, predicting consistent activities, and actively seeking information to predict inconsistent behaviors. We propose SLaTe-PRO (Sequential Latent Temporal model for Predicting Routine Object usage), which improves upon prior state-of-the-art by combining object and user action information, and conditioning object usage predictions on past history. Additionally, we find some human behavior to be inherently stochastic and lacking in contextual cues that the robot can use for proactive assistance. To address such cases, we introduce an interactive query mechanism that can be used to ask queries about the user's intended activities and object use to improve prediction. We evaluate our approach on longitudinal data from three households, spanning 24 activity classes. SLaTe-PRO performance raises the F1 score metric to 0.57 without queries, and 0.60 with user queries, over a score of 0.43 from prior work. We additionally present a case study with a fully autonomous household robot.