Predicting Routine Object Usage for Proactive Robot AssistanceDownload PDF

Published: 30 Aug 2023, Last Modified: 17 Oct 2023CoRL 2023 PosterReaders: Everyone
Keywords: Proactive Robot Assistance, User Routine Understanding, Interactive Clarification, Robot Learning
TL;DR: We propose SLaTe-PRO, a model which can learn to anticipate user's needs from past observations and use them to provide proactive assistance, and an interactive clarification mechanism, which can further refine such predictions.
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.
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