arXiv:2607. 26345v1 Announce Type: new Abstract: Modeling and forecasting nonlinear dynamics under distribution shifts is essential for robust decision-making in real-world systems.
arXiv:2606. 23444v2 Announce Type: replace-cross Abstract: Accurate dynamics models are critical for informed decision-making in robotic systems, particularly for agile aerial vehicles operating under uncertainty.
Autonomous driving requires long-horizon closedloop decision making in dynamic traffic environments. Latent world models offer an effective framework for this problem by enabling imagination-based decision making in compact latent spaces.
arXiv:2606. 16480v1 Announce Type: cross Abstract: Robots deployed in the real world must plan motions across diverse scenarios without per-scenario retuning.
arXiv:2607. 11964v1 Announce Type: new Abstract: Autonomous driving requires long-horizon closedloop decision making in dynamic traffic environments.
arXiv:2608. 02519v1 Announce Type: new Abstract: Effective model-based reinforcement learning in stochastic environments requires planning that accounts for predictive uncertainty.
Effective model-based reinforcement learning in stochastic environments requires planning that accounts for predictive uncertainty. Propagating full state distributions analytically offers a principled way to do this, but has traditionally required restrictive policy or reward structures to remain tractable.
The paper introduces Interaction-Constrained Drive Policy (ICDP), an offline reinforcement learning framework for autonomous driving that explicitly controls interaction-level distribution shift. ICDP decomposes joint-support degradation into ego-support and interaction-support components, estimating the latter via contrastive density-ratio estimation without requiring joint-density modeling or simulator rollouts. Experiments on nuPlan, Interplan, and real-world truck data demonstrate that ICDP reduces selection of high-value but interaction-unsupported trajectories and improves performance in interaction-critical driving scenarios.
This tutorial paper reviews a decade of advances at the intersection of machine learning and control, focusing on methods that blend machine learning flexibility with control theory’s structure to create capable, reliable, and safe autonomous systems. It covers core concepts for stability and safety, learning dynamics models to handle uncertainty, and data‑driven policy synthesis techniques such as reinforcement learning, imitation learning, and Bayesian optimization. Representative applications and open challenges are discussed, and the authors provide implementations, examples, and additional resources on their website.
arXiv:2606. 06423v1 Announce Type: cross Abstract: Safety-critical traffic scenario generation is essential for evaluating autonomous driving systems under rare but high-risk interactions.
arXiv:2609.39888v2 Announce Type: new Abstract: Neural trajectory predictors can reach low prediction error while violating dynamics, actuator limits, or state constraints, especially when controls a...
arXiv:2609.24749v1 Announce Type: cross Abstract: Latent world models predict the consequences of actions, but accurate prediction does not guarantee that latent distance reflects which candidate wil...
The paper introduces WM‑RMoE, a World Model‑based Risk‑aware Mixture‑of‑Experts framework for autonomous overtaking. It uses a learned latent dynamics model to perform multi‑step rollouts, evaluating cumulative risk at the trajectory level, and employs a hierarchical gating mechanism to coordinate long‑, short‑horizon, and rule‑based safety experts. A Gaussian Mixture Model preserves multimodal maneuver branches, improving robustness and preventing behavioral averaging, leading to better safety compliance, decision stability, and generalization in experiments.