Think Fast, Plan Selectively: Adaptive Deliberation for Efficient Data-Driven MPC
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 a...
| Authors | Yi Xian Goh, Sze Jue Yang, Hao Luan |
|---|---|
| Primary category | cs.RO |
| Published | 26 September 2026 |
| Last updated at source | 30 September 2026 |
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