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arXiv · 2606.23079

AdaReP:Adaptive Re-Planning under Model Mismatch for Neural World-Model Predictive Control

Yutian Cheng·Xiaojian Ma·Xianhao Wang·Min Yang·Rongpeng Su·Hangxin Liu·Xi Chen·Shuai Li·Qing Li
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Abstract

Neural world models coupled with model predictive control (MPC) replan at every environment step to bound accumulated prediction error, but this incurs substantial computational overhead. Reusing a cached plan reduces this overhead, yet its effectiveness depends on how prediction mismatch propagates through the local dynamics. We analyze this trade-off with a perturbation-based dynamic-regret framework and show that stale-plan penalties scale with the reuse tolerance, the accumulated mismatch since the last replanning step, and the local dynamics sensitivity. Based on this structure, we propose AdaReP, a training-free wrapper that adapts the replanning tolerance online using the current deviation from the cached rollout and a local sensitivity estimate, without modifying the learned world model or planner. Across image-space planning, latent-space control, and real-world robotic manipulation, AdaReP substantially reduces planner-side computation while maintaining comparable task performance, including over 80% fewer queries on a 50-trial physical robot study.

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Yutian Cheng, Xiaojian Ma, Xianhao Wang, Min Yang, Rongpeng Su, Hangxin Liu, Xi Chen, Shuai Li, Qing Li. 2026-06-22. AdaReP:Adaptive Re-Planning under Model Mismatch for Neural World-Model Predictive Control. https://arxiv.org/abs/2606.23079

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