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.

翻译：摘要：机器人辅助中的主动性指的是机器人无需明确指令，能预判用户需求并执行辅助行为的能力。这要求机器人理解用户常规活动、预测一致性行为，并主动收集信息以预测异常行为。我们提出SLaTe-PRO模型（用于预测日常物品使用的序贯潜在时序模型），通过融合物品与用户动作信息、基于历史记录优化物品使用预测，改进了现有最优方法。此外，我们发现部分人类行为具有固有随机性，且缺乏可供机器人用于主动辅助的上下文线索。针对此类情况，我们引入交互式查询机制，可向用户询问其意图活动及物品使用以优化预测。我们在涵盖24类活动、来自三个家庭的纵向数据上评估了该方法。无查询状态下，SLaTe-PRO将F1分数提升至0.57，引入用户查询后达0.60，而此前方法仅为0.43。我们进一步展示了全自主家居机器人实验案例研究。