PACT: Robots that ask before acting improve long-term collaboration
New AI framework lets robots know when to ask for help instead of guessing wrong.
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The paper introduces PACT (Proactive Asking for Continual Task Assistance), an ask-or-act framework for human-robot collaboration. It uses reinforcement learning and cross-day interaction history to evaluate contextual sufficiency before acting, and introduces a clarification utility metric to balance assistance accuracy and asking frequency. In multi-day experiments, PACT consistently improves both assistance accuracy and clarification utility compared to passive inference baselines.
- PACT uses reinforcement learning to decide when to ask for clarification versus when to act, leveraging cross-day interaction history.
- It introduces a clarification utility metric that quantifies the trade-off between assistance accuracy and the frequency of user interruptions.
- In multi-day experiments, PACT outperformed passive inference baselines in both assistance accuracy and overall utility.
Why It Matters
Enables robots to learn user preferences over time, making long-term home or office assistance more reliable and less frustrating.