Robotic assistants in long-term human-robot collaboration need to assist users under partial observations while leveraging cross-day interaction history. However, human traits and routines are often unknown at the beginning of collaboration, making passive infer-then-act assistance ineffective and inefficient. To address this challenge, we study a cross-day proactive asking setting for continual task assistance and propose PACT (Proactive Asking for Continual Task Assistance), an ask-or-act framework that determines whether clarification should be sought before taking action. PACT leverages current observations together with accumulated interaction history to evaluate contextual sufficiency, enabling the robot to provide more reliable assistance and progressively adapt to the user over time. We implement its primary learned instantiation using reinforcement learning and evaluate alternative instantiations under the same framework. To assess such behavior, we further introduce a clarification utility metric that quantifies the trade-off between assistance accuracy and the frequency of clarification requests. Experiments in multi-day embodied collaboration scenarios demonstrate that, compared with passive inference baselines, PACT consistently improves both assistance accuracy and clarification utility, highlighting the importance of proactive asking in continual human-robot collaboration.

翻译：摘要：在长期人机协作中，机器人助手需要在部分观测条件下利用跨日交互历史来协助用户。然而，人类特征与日常行为模式在协作初期往往未知，这使得被动的“先推断后行动”协助方式既低效又效果不佳。针对这一挑战，我们研究了跨日主动询问机制以支持持续任务协助，并提出了PACT（持续任务协助的主动询问框架）——一个决定在执行动作前是否应寻求澄清的“询问-行动”框架。PACT结合当前观测与累积的交互历史评估上下文充分性，使机器人能够提供更可靠的协助，并随时间逐步适应用户。我们使用强化学习实现了其主要的学习实例，并在同一框架下评估了替代实例。为评估这种行为，我们进一步引入澄清效用指标，量化协助准确性与澄清请求频率之间的权衡。在多日具身协作场景实验中，与被动推断基线相比，PACT在协助准确性和澄清效用上均取得一致提升，凸显了主动询问在持续人机协作中的重要性。