From Visual Search to Movement Control: A Priority Field for Artificial Agents

Han Zhang; Zhong Cao

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arXiv · 2609.30704

From Visual Search to Movement Control: A Priority Field for Artificial Agents

Han Zhang·Zhong Cao

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Abstract

Human spatial attention is widely conceptualized as being guided by a priority map that integrates perceptual salience, current goals, and past experiences. Here, we extend priority-based computation to movement control in artificial agents. We first introduce a lightweight model of visual search based on an integrated priority map. Trained on human saccades, it reproduced key behavioral patterns, including oculomotor suppression and history-driven selection. Extending the search model, we equipped an artificial agent with a priority field and evaluated its performance in a reach-avoid task that required reaching a goal destination while avoiding moving obstacles. Compared with alternative architectures, priority-field agents trained more efficiently and performed better in unseen, complex scenarios, even from simple demonstrations. Adding a simple memory mechanism also produced human-like, history-driven effects in anticipating the likely location of the upcoming goal. These findings suggest that priority-based computation may provide a promising foundation for movement control in artificial agents.

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Han Zhang, Zhong Cao. 2026-09-25. From Visual Search to Movement Control: A Priority Field for Artificial Agents. https://arxiv.org/abs/2609.30704

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