[Submitted on 26 Sep 2026 (v1), last revised 6 Nov 2026 (this version, v3)]
Abstract:Data-driven model predictive control (MPC) combines learned world models with online trajectory optimization, achieving strong performance in continuous control. However, the per-step cost of sampling and evaluating hundreds of candidate trajectories restricts deployment to control frequencies well below what real-time robotics demands. Motivated by the dual-process theory of human cognition, which distinguishes between fast, intuitive processing (System 1) and slower, deliberative reasoning (System 2), we ask whether every decision requires the same degree of computational deliberation. We propose Fast-TD-MPC, a lightweight framework that adaptively routes between fast policy execution and test-time planning, reserving costly deliberation for states where it is most needed. Fast-TD-MPC delivers competitive task performance across 103 continuous control tasks while achieving up to ~4x faster inference. Under external disturbances, Fast-TD-MPC selectively falls back to planning, maintaining robustness comparable to the original planner.
Submission history
From: Yi Xian Goh [view email]
[v1] Sat, 26 Sep 2026 13:10:00 UTC (1,765 KB)
[v2] Wed, 30 Sep 2026 02:14:08 UTC (1,765 KB)
[v3] Fri, 6 Nov 2026 02:59:16 UTC (1,765 KB)
