We study trajectory optimization in mobile sensing systems that must identify which member of a finite candidate set is the true target, while maintaining reachability to all potential candidate targets, under resource constraints. Deferred-Decision Trajectory Optimization (DDTO) addresses this setting by computing trajectories that reach individual targets but remain coincident for as long as possible before separating toward different targets.
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.
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.
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.
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...
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...
arXiv:2608. 14466v1 Announce Type: cross Abstract: An autonomous robot efficiently exploring an unknown environment, such as looking for water sources on Mars, faces two simultaneous demands: building an accurate information map while quickly finding the regions of greatest value, and paying for every meter of travel and the cost of every measurement it takes.
arXiv:2606. 11019v1 Announce Type: cross Abstract: Learning-based motion planners, despite recent progress, often suffer from temporal inconsistency.
arXiv:2609.36712v1 Announce Type: cross Abstract: Accurately learning nonlinear dynamics from a finite-duration experiment requires the efficient collection of informative data. We address this chall...
Expand-and-Prune is a test‑time framework for candidate‑based end‑to‑end autonomous driving planners that dynamically expands and prunes the set of trajectory hypotheses. It adds new candidates guided by semantic driving intent and temporally propagated predictions, then uses uncertainty in intermediate trajectory scores to prune the expanded set. Across four planners and four benchmarks, the method consistently improves planning performance, achieving state‑of‑the‑art results on LangAuto and significant gains on Bench2Drive, Fail2Drive, and NAVSIMv2, especially in out‑of‑distribution scenarios.
arXiv:2510. 07750v3 Announce Type: replace-cross Abstract: Robust optimization safeguards decisions against uncertainty by optimizing against worst-case scenarios, yet their effectiveness hinges on a prespecified robustness level that is often chosen ad hoc, leading to either insufficient protection or overly conservative and costly solutions.
arXiv:2608. 01130v1 Announce Type: new Abstract: A broad range of models face the mismatch where they are updated through trajectory losses but are evaluated by downstream task reward.