arXiv:2604.02025v1 Announce Type: cross
Abstract: In this work, we extend our systematic capacity region perspective to multi-junction traffic networks, focussing on the special case of an urban corridor network. In particular, we train and evaluate c…
arXiv:2604.01912v1 Announce Type: cross
Abstract: This work formalizes the differential topology of redundancy resolution for systems governed by signed-quadratic actuation maps. By analyzing the minimally redundant case, the global topology of the co…
arXiv:2604.02196v1 Announce Type: cross
Abstract: Many communication and control problems are cast as multi-objective Markov decision processes (MOMDPs). The complete solution to an MOMDP is the Pareto front. Much of the literature approximates this f…
arXiv:2604.01991v1 Announce Type: new
Abstract: In recent years, the integration of additive manufacturing (AM) and industrial robotics has opened new perspectives for the production of complex components, particularly in the automotive sector. Robot-…
arXiv:2604.01459v1 Announce Type: cross
Abstract: Data-driven approximations of the infinite-dimensional Koopman operator rely on finite-dimensional projections, where the predictive accuracy of the resulting models hinges heavily on the invariance of…
arXiv:2604.02256v1 Announce Type: new
Abstract: This paper proposes a new, robust method to solve the inverse kinematics (IK) of multi-segment continuum manipulators. Conventional Jacobian-based solvers, especially when initialized from neutral/rest c…
arXiv:2601.19462v3 Announce Type: replace-cross
Abstract: This paper aims to provide a clear and rigorous understanding of commonly recognized safety constraints in physical human-robot interaction, particularly regarding ISO/TS 15066. We investigate …
arXiv:2604.01477v1 Announce Type: new
Abstract: Reinforcement learning (RL) and model predictive control (MPC) offer complementary strengths, yet combining them at scale remains computationally challenging. We propose soft MPCritic, an RL-MPC framewor…
arXiv:2504.04665v2 Announce Type: replace
Abstract: Scientific machine learning is an emerging field that broadly describes the combination of scientific computing and machine learning to address challenges in science and engineering. Within the conte…
arXiv:2604.00553v1 Announce Type: new
Abstract: The scenario approach provides a powerful data-driven framework for designing solutions under uncertainty with rigorous probabilistic robustness guarantees. Existing theory, however, primarily addresses …