Brain-Inspired Prior Regulation for Efficient Planning with World Models

Advances in world models enable predictive reasoning about action consequences, supporting goal-directed decision-making in robotic manipulation and visual navigation. However, action search remains computationally expensive, and existing prior-guided methods improve sampling efficiency through initialization but lack persistent regulation of subsequent optimization, leaving search vulnerable to deviations from the learned action prior. To address this limitation, we propose Brain-Inspired Prior Regulation (BIPR), a framework for efficient planning with world models through coordinated prior regulation across multiple timescales. Specifically, prior anchoring penalizes deviations from the action prior throughout search, uncertainty–conflict arbitration adapts regularization to current search evidence, and metaplastic calibration uses offline learning history to modulate this adaptive response. Experiments on established benchmarks for world-model-based control show that BIPR improves macro-average success rates with LeWorldModel and Sub-JEPA by 30.33 and 26.33 percentage points, respectively, over the MPPI baseline, while complementary efficiency evaluations demonstrate a 46.73% reduction in evaluation wall time relative to CEM under a matched candidate budget. Beyond task success, computational cost analysis shows that BIPR requires no additional trainable parameters compared with prior-guided planning, while incurring only 0.82% additional evaluation overhead. These findings suggest that brain-inspired prior regulation offers a promising approach to improving the performance–efficiency trade-off of world-model-based planning.