PACT: Proactive Asking for Continual Task Assistance in Human-Robot Collaboration
Robotic assistants in long-term human-robot collaboration need to assist users under partial observations while leveraging cross-day interaction history. Since human traits are often unknown initially, passive infer-then-act is ineffective. We propose PACT, an ask-or-act framework that evaluates contextual sufficiency to decide whether to seek clarification before acting. Using reinforcement learning, PACT improves assistance accuracy and clarification utility over passive baselines in multi-day embodied scenarios.
Article intelligence
Key points
- PACT framework enables robots to proactively ask for clarification when needed, improving assistance reliability.
- Implemented via reinforcement learning, introducing a clarification utility metric. Outperforms passive inference in multi-day collaborations.
Why it matters
This matters because PACT framework enables robots to proactively ask for clarification when needed, improving assistance reliability.
Technical impact
May affect model selection, inference cost, product capability, and evaluation benchmarks.
[2605.24350] PACT: Proactive Asking for Continual Task Assistance in Human-Robot Collaboration
[Submitted on 23 May 2026]
Title:PACT: Proactive Asking for Continual Task Assistance in Human-Robot Collaboration
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Abstract: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.
Subjects:
Robotics (cs.RO); Human-Computer Interaction (cs.HC)
Cite as: arXiv:2605.24350 [cs.RO]
(or arXiv:2605.24350v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2605.24350
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Chengbo He [view email] [v1] Sat, 23 May 2026 02:22:02 UTC (21,270 KB)
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