arXiv:2604.02821v1 Announce Type: new
Abstract: This paper presents a learning-based approach for all-pairs motion planning, where the initial and goal states are allowed to be arbitrary points in a safe set. We construct smooth goal-conditioned neura…
arXiv:2604.02878v1 Announce Type: new
Abstract: In GNSS-denied underwater environments, individual unmanned underwater vehicles (UUVs) suffer from unbounded dead-reckoning drift, making collaborative navigation crucial for accurate state estimation. H…
arXiv:2603.29512v2 Announce Type: replace
Abstract: The reliable operation of Unmanned Underwater Vehicle (UUV) clusters is highly dependent on continuous acoustic communication. However, this communication method is highly susceptible to intermittent…
arXiv:2512.24493v2 Announce Type: replace-cross
Abstract: Control barrier functions for port-Hamiltonian systems inherit model uncertainty when the Hamiltonian is learned from data. We show how to propagate this uncertainty into a safety filter with i…
arXiv:2604.03086v1 Announce Type: cross
Abstract: This work establishes a rigorous bridge between infinite-dimensional delay dynamics and finite-dimensional Koopman learning, with explicit and interpretable error guarantees. While Koopman analysis is …
arXiv:2604.02707v1 Announce Type: cross
Abstract: Humanoid robot technologies have demonstrated immense potential for minimally invasive surgery (MIS). Unlike dedicated multi-arm surgical platforms, the inherent dual-arm configuration of humanoid robo…
arXiv:2604.03132v1 Announce Type: cross
Abstract: Safety-critical autonomous systems must satisfy hard state constraints under tight computational and sensing budgets, yet learning-based controllers are often far more complex than safe operation requi…
arXiv:2604.02401v1 Announce Type: new
Abstract: This paper revisits three backup-based safety filters — Backup Control Barrier Functions (Backup CBF), Model Predictive Shielding (MPS), and gatekeeper — through a unified comparative framework. Using …
arXiv:2508.02441v2 Announce Type: replace-cross
Abstract: Model predictive control (MPC) is widely used in process control due to its interpretability and ability to handle constraints. As a parametric policy in reinforcement learning (RL), MPC offers…
arXiv:2512.17466v3 Announce Type: replace-cross
Abstract: Optimal AP clustering and power allocation are critical in user-centric cell-free massive MIMO systems. Existing deep learning models lack flexibility to handle dynamic network configurations. …