Problem
Research questions and friction points this paper is trying to address.
proactive robots
robot assistance
offline evaluation
closed-loop evaluation
human-robot interaction
Robots That Take Initiative: A Framework for Building and Evaluating Proactive Robots
🤖 AI Summary
This study addresses the absence of a unified formal definition for active assistive robots and the tendency of existing offline evaluations to overestimate performance. To overcome these limitations, this work establishes a three-level active assistance framework and proposes the GAP method, which integrates passive observational learning, goal prediction, and reinforcement learning. Furthermore, it introduces the first unified formalization system alongside a closed-loop simulation evaluation mechanism incorporating an adaptive human model. The proposed approach effectively validates the autonomous decision-making capabilities of robotic systems. Experimental results demonstrate that GAP exhibits robust performance in closed-loop testing, significantly outperforming existing state-of-the-art methods without imposing additional cognitive burden on users.
📝 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Proactive Robot Assistance
Closed-loop Evaluation
Goal Anticipation
Passive Observation Learning
Adaptive Human Model
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