An integrative computational approach for obstacle avoidance during action selection
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Action selection in cluttered environments, where individuals must simultaneously pursue goals and avoid obstacles, presents a significant challenge for the brain. To understand the underlying mechanisms of action selection in such contexts, we propose a computational model that extends stochastic optimal control theory through a novel framework for obstacle avoidance. The model decomposes action selection as a weighted combination of individual control policies, each generated for either target approach or obstacle avoidance. By integrating value information from goals, obstacles, and actions into a unified measure of “relative desirability”, the model dynamically determines the contribution of each policy to the overall action selection process. We evaluated the framework using simulated target-reaching tasks in cluttered environments based on previous human studies. The results showed that the model captures key features of human motor behavior, including the influence of obstacle properties on movement trajectories and the transient tendency to initiate movements toward obstacles before avoidance. This work offers new insights into the dynamic interaction between approach and avoidance behaviors, providing a comprehensive framework for understanding action selection in complex and naturalistic settings.
Author Summary
Every day, we make countless movements that require us to reach for something while avoiding obstacles—for example, grabbing a cup without knocking over nearby objects. How the brain selects such actions in cluttered environments remains poorly understood. In this study, we developed a computational model that explains how people plan and control movements when both goals and obstacles are present. Our model assumes that the brain prepares multiple potential actions at once—some for reaching the goal and others for avoiding obstacles—and then combines them based on how desirable each option is at any moment. Using computer simulations of reaching movements, we found that the model reproduced key patterns observed in human behavior, including the brief tendency to move toward an obstacle before steering away from it. By showing how the brain might continuously balance approach and avoidance drives, our work offers a new way to understand how people make rapid and flexible movement decisions in complex, everyday settings.
