arXiv:2608. 09628v1 Announce Type: new Abstract: Collision avoidance systems are commonly used to avoid fragmentation events occurring in Low-Earth Orbit (LEO) and Geosynchronous Equatorial Orbit (GEO).
By Logan Luna (Georgia Institute of Technology), Juan Ortiz Couder (Embry-Riddle Aeronautical University), Raul Alejandro Vargas-Acosta (Embry-Riddle Aeronautical University)
The paper presents a chance-constrained belief-space planning framework for autonomous collision avoidance in low Earth orbit. It models uncertain orbital states as Gaussian beliefs and uses a Monte Carlo tree search to decide whether to wait for better tracking data or to execute a maneuver before the time of closest approach. Experiments on 96 scenarios from NASA’s dataset show that the planner can avoid maneuvers in about 40% of cases while keeping collision risk below the threshold, with performance heavily dependent on tracking quality and cadence.
By Grace Ra Kim, Duncan Eddy, Mykel J. Kochenderfer
arXiv:2607. 10014v1 Announce Type: cross Abstract: Learning-based separation assurance for small Unmanned Aircraft Systems (sUAS) achieves near-zero collision rates in simulation, but assumes accurate position and velocity information from Global Navigation Satellite Systems (GNSS).
By Alex Zongo, Peng Wei
The paper presents a method to estimate inconsistency response surfaces in Cyber‑Physical Systems (CPS) under uncertainty. By reformulating inconsistency as an intervention‑response modeling problem, the authors use Saltelli sampling and multi‑fidelity Monte Carlo estimation to generate datasets, then train a surrogate model that predicts inconsistency from propagated uncertainty geometry. Experiments on 48 scenarios across 10 CPS domains show that the surrogate matches Monte Carlo estimates while dramatically reducing evaluation time, enabling extensive sensitivity analysis and a gradient‑based consistency recourse method to identify minimal interventions that restore consistency.
By Johannes M\"akelburg, Tim Schwabe, Maribel Acosta
arXiv:2607. 27933v3 Announce Type: replace Abstract: Flow matching (FM) has become a popular action head paradigm for modern embodied models.
By Ziyang Rao, Yiren Zhao, Weiyu Guo, Ben Fei, Yandong Guo, Hui Xiong
arXiv:2607. 16591v1 Announce Type: cross Abstract: The RLxF programme argues that learning signals should come from world feedback rather than from internal model proxies.
By Zhaohui Wang