No Plan, Yet Human: A Reactive Robotics Model Predicts Human Planning Failures on a Clinical Task

2026-05-15

AI Summary

This study investigates why certain sequential planning tasks pose greater challenges for humans, particularly those with impaired planning abilities, and accurately replicates their characteristic failure patterns. For the first time, the purely reactive robotic framework AICON is adapted to clinical cognitive assessment—specifically, the Tower of London task—without relying on lookahead planning or prior assumptions about human cognition. Using leave-two-out cross-validation across 24 problems, AICON not only outperforms difficulty predictions based solely on task structure but also significantly surpasses conventional planning baselines, demonstrating strong generalization to unseen problems. The findings suggest that human behavior under diminished planning capacity converges toward reactive mechanisms and enable precise ranking of task difficulty across distinct patient populations.

Abstract

Understanding why some sequential planning problems are harder than others requires models that go beyond average performance. They should capture the specific pattern of which problems are hard, and ideally fail in the same way people do when planning capacity is reduced. We apply AICON, a reactive gradient-descent framework developed for robotic manipulation, to the Tower of London test, a cognitive test used to assess planning in Parkinson's disease, mild cognitive impairment, and stroke. Without any lookahead planning or knowledge of human cognition, AICON reproduces the fine-grained human difficulty ordering across 24 problems better than structural task parameters and generalizes to held-out problems in a leave-two-out evaluation. Crucially, AICON outperforms a planning baseline for groups with reduced planning capacity while the planning baseline better captures healthy controls. This dissociation was predicted by the original AICON paper, which noted that the model's failure modes resemble those of Parkinson's patients who struggle with goal hierarchies but not move counts. This suggests that as planning capacity is reduced, human behavior shifts toward the reactive mode AICON models. The finding extends a broader pattern: AICON, originally built for robotics, now captures aspects of biological behavior across perception, eye movements, and sequential planning, suggesting its core abstraction reflects something real about how biological systems are organized.

Problem

• sequential planning
• planning failures
• Tower of London
• cognitive impairment
• reactive behavior

Innovation

• reactive robotics
• planning failure
• Tower of London
• AICON
• cognitive modeling