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

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.

Robotics (cs.RO)

arXiv:2609.30704 [cs.RO]

(or arXiv:2609.30704v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2609.30704 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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