The paper introduces a conflict‑predictive variable horizon for distributed model predictive control in multi‑drone collision avoidance. Each drone locally estimates future conflicts using observed positions and confidence funnels, then selects the minimal horizon that covers the farthest predicted conflict, shrinking in clear air and expanding only when necessary. The authors prove that this adaptive horizon preserves recursive feasibility and asymptotic stability for linear models, and demonstrate in simulation that it reduces per‑step solver cost and total computation while maintaining separation on dense benchmarks.
By Linda M\"{u}mken, Michael Schwung, Stefan Lier, Andreas Schwung
The paper introduces WONDER, a radio world‑model‑based negotiation framework designed to optimize multi‑UAV coverage for rapid post‑disaster wireless restoration. WONDER employs a Joint‑Embedding Predictive Architecture to forecast the incremental radio impact of candidate UAV trajectories and uses multi‑round negotiation to sequentially commit trajectories while updating the context. Experiments in the RadioDynamics simulation environment demonstrate that WONDER outperforms six other methods, achieving a balanced score of 0.870 and a 0.162 coverage advantage over STACCA while preserving full UAV connectivity.
By Jiahao Huang, Rongpeng Li, Zhifeng Zhao, Guoru Ding, Honggang Zhang
arXiv:2603. 21180v4 Announce Type: replace Abstract: Sequential experimental design under expensive, gradient-free objectives is a central challenge in computational statistics: evaluation budgets are tightly constrained and information must be extracted efficiently from each observation.
By Foo Hui-Mean, Yuan-chin I Chang
arXiv:2608. 09166v1 Announce Type: cross Abstract: Reliable uncertainty representation is essential for deploying autonomous systems that interact with their environment, as robots must reason about how uncertainty arising from both stochasticity and model mismatch is impacted by contacts with obstacles (e.
By Lu\'is Marques, Kristian Popov, Dmitry Berenson
The paper presents a convergence framework for deep $V$‑learning over a finite horizon $H$, deriving explicit bounds on policy loss by decomposing the Bellman update error into six residuals. It shows how $L^s$ concentrability controls expected $L^1$ loss, quantifies the impact of shared sampling across horizon levels, and provides optimal and near‑optimal sample allocations for statistical error rates. The work also establishes sharp action‑gap bounds under a margin condition, transfers optimal‑gap results to frozen‑iterate gaps, and offers consistency guarantees for generative‑reset approximate‑ERM procedures with exact action scores.
By Yury Kolomeytsev
arXiv:2610.09226v1 Announce Type: cross
Abstract: Data collection using Unmanned Aerial Vehicles (UAVs) is challenging when LoRa IoT Devices (IoTDs) duty-cycle to conserve battery. Under energy const...
By Naresh Babu Kakarla, V. Mahendran