arXiv:2606. 15359v1 Announce Type: new Abstract: Diffusion models have emerged as powerful tools for planning and control by learning multimodal distributions over actions and trajectories.
By Paolo Giaretta, Zeyang Li, Navid Azizan
The paper introduces uncertainty-aware extensions of the X-TRACK physics‑aware trajectory prediction framework, namely X-TRACK-DE and X-TRACK-MCD. These extensions predict future vehicle motion variables while explicitly modeling and propagating both aleatoric and epistemic uncertainties through vehicle dynamics, rather than only in trajectory space. Conformal prediction is then applied to the trajectory‑space covariance to create uncertainty regions that achieve a desired marginal coverage level, and experiments on the highD dataset demonstrate improved prediction accuracy and calibrated uncertainty estimates.
By Aanchal Rajesh Chugh, Sebastian Dorn
arXiv:2608. 07751v1 Announce Type: cross Abstract: Safe and efficient robot navigation in crowds requires anticipating pedestrian motion despite uncertain and potentially shifting prediction errors.
By Cheng Guo, Mingzhe Ni, Zheng Liang, Yihu Ling, Yuan Hu, Michele Caprio, Daniele Pucci, Wei Pan
Accurately learning nonlinear dynamics from a finite-duration experiment requires the efficient collection of informative data. We address this challenge for stochastic controlled nonlinear dynamical...
arXiv:2610.14307v1 Announce Type: new
Abstract: We propose ConicFlow, which frames constrained sampling from a flow matching model at inference time as an optimal control problem that balances deviat...
By Shervin Khalafi, Kasra Fallah, Sung Cho, Dongsheng Ding, James Anderson, Alejandro Ribeiro
arXiv:2609.26021v1 Announce Type: new
Abstract: Dynamic black-box optimization presents significant challenges for Bayesian Optimization (BO), as the objective function evolves over time, causing opt...
By Merlin Angel Kelly, Rishan Patel, Alexander Thomas, Ziyue Zhu, Zikun Quan, Tom Carlson, Youngjun Cho