arXiv Machine Learning
Think Fast, Plan Selectively: Adaptive Deliberation for Efficient Data-Driven MPC
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
Related stories
arXiv AI
Sep 2
Dual Process Motion Planning
The paper introduces a dual‑process architecture for nonlinear motion planning that blends fast, learning‑based intuition (System‑1) with slow, robust symbolic reasoning (System‑2). A metacognitive controller decides when to use each component, aiming to balance speed, precision, and adaptability. Experiments on diverse benchmark environments show consistent improvements in planning efficiency, accuracy, and generalization, highlighting the benefits of integrating learning with structured reasoning.
arXiv AI
Sep 1
AutoCRAT: Within-trajectory Joint Control of Stochasticity and Compute for LLM Reasoning
arXiv:2608.29988v1 Announce Type: new Abstract: Large language models (LLMs) achieve strong reasoning performance, which depends critically on inference-time decisions. Yet these decisions are common...
arXiv Machine Learning
Aug 10
Fast and Accurate: An Adaptive VLA Inference Framework through Environment-aware Model Selection
arXiv:2608. 06434v1 Announce Type: cross Abstract: Embodied intelligence demands both long-horizon reasoning and real-time closed-loop responsiveness.
arXiv AI
Jun 16
HOLO-MPPI: Multi-Scenario Motion Planning via Hierarchical Policy Optimization
arXiv:2606. 16480v1 Announce Type: cross Abstract: Robots deployed in the real world must plan motions across diverse scenarios without per-scenario retuning.
arXiv AI
Jul 21
When to Plan: Learning to Select Between Reactive Control and Deliberative Planning
arXiv:2607. 16421v1 Announce Type: new Abstract: It has long been recognized that humans have the ability to switch between fast, reactive decision-making and slower, deliberative planning.
arXiv AI
Aug 14
BrainWAM: Action-Space Coordination of Semantic Priors and Predictive Dynamics for Autonomous Driving
arXiv:2608. 12854v1 Announce Type: cross Abstract: Autonomous driving requires planning under both semantic constraints and predictive dynamics.