Proactive robot assistance enables a robot to anticipate and provide for a
user's needs without being explicitly asked. We formulate proactive assistance
as the problem of the robot anticipating temporal patterns of object movements
associated with everyday user routines, and proactively assisting the user by
placing objects to adapt the environment to their needs. We introduce a
generative graph neural network to learn a unified spatio-temporal predictive
model of object dynamics from temporal sequences of object arrangements. We
additionally contribute the Household Object Movements from Everyday Routines
(HOMER) dataset, which tracks household objects associated with human
activities of daily living across 50+ days for five simulated households. Our
model outperforms the leading baseline in predicting object movement, correctly
predicting locations for 11.1% more objects and wrongly predicting locations
for 11.5% fewer objects used by the human user.

通过生成式图神经网络，学习了一种统一的空间 - 时间预测模型对象动态，以适应人类的日常生活活动，并在 HOMER 数据集中优于现有的预测方法约 11.1％。

时空物体建模实现机器人辅助

Proactive Robot Assistance via Spatio-Temporal Object Modeling

Opioid overdose is a growing public health crisis in the United States. This
crisis, recognized as "opioid epidemic," has widespread societal consequences
including the degradation of health, and the increase in crime rates and family
problems. To improve the overdose surveillance and to identify the areas in
need of prevention effort, in this work, we focus on forecasting opioid
overdose using real-time crime dynamics. Previous work identified various types
of links between opioid use and criminal activities, such as financial motives
and common causes. Motivated by these observations, we propose a novel
spatio-temporal predictive model for opioid overdose forecasting by leveraging
the spatio-temporal patterns of crime incidents. Our proposed model
incorporates multi-head attentional networks to learn different representation
subspaces of features. Such deep learning architecture, called
"community-attentive" networks, allows the prediction of a given location to be
optimized by a mixture of groups (i.e., communities) of regions. In addition,
our proposed model allows for interpreting what features, from what
communities, have more contributions to predicting local incidents as well as
how these communities are captured through forecasting. Our results on two
real-world overdose datasets indicate that our model achieves superior
forecasting performance and provides meaningful interpretations in terms of
spatio-temporal relationships between the dynamics of crime and that of opioid
overdose.

利用实时犯罪动态预测阿片类药物过量，并提出基于区域群组的深度学习架构，它能够自适应地优化位置预测，为预防阿片类药物过量提供有意义的解释。