  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.
