Robots That Take Initiative: A Framework for Building and Evaluating Proactive Robots

Abstract:Effective robot assistance beyond narrow roles and repetitive tasks requires robots to be proactive - to decide what needs to be done rather than waiting to be told. While proactivity is increasingly explored, it lacks a unified formulation, and work in the domain is typically evaluated offline against static human models that cannot capture the effect of a robot's actions on the environment and the user's own behavior. We introduce a unified formalism for proactive robot assistance, organize it into three levels, and provide a framework to address the highest level of unprompted proactive assistance. We then show that offline evaluation overstates performance in this setting, and contribute a closed-loop evaluation with a human model that adapts to the robot. Finally, we present a method, GAP, that instantiates our framework, learning from passive observation to anticipate user goals and act. Under closed-loop evaluation, prior state-of-the-art methods collapse, in some cases adding more work than they save, while GAP remains robust and substantially outperforms them.

Robotics (cs.RO); Artificial Intelligence (cs.AI)

arXiv:2609.28910 [cs.RO]

(or arXiv:2609.28910v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2609.28910 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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