322 papers
This paper evaluates small language models for real-time leader-follower role classification in human-robot interaction, demonstrating that zero-shot fine-tuning on a novel dataset achieves high accuracy and low latency on edge devices, whereas one-shot adaptation suffers from performance degradation due to increased context complexity.
This paper introduces DRAFTO, a novel trajectory optimization algorithm for robotic manipulators that decouples reduced-space Gauss-Newton descent with adaptive feasibility repair to efficiently generate smooth, safe, and constraint-compliant paths, demonstrating superior performance over existing planners in diverse and complex manipulation tasks.
This paper introduces TATIC, a unified framework that combines torque-based force estimation with a task-aware Temporal Convolutional Network to infer both discrete task-level intent and continuous motion parameters from brief physical corrections, enabling robust online adaptation in human-robot collaboration.
This paper investigates the impact of parameter selection on the convergence of a modified ant colony algorithm to optimize the hierarchical structure of an industrial distributed control system, offering tuning recommendations applicable to broader combinatorial optimization problems.
This paper extends the Irreversible Port-Hamiltonian Systems (IPHS) framework to model non-isentropic fluids with viscous dissipation in both Eulerian and Lagrangian coordinates by adapting differential operators for convective transport and defining boundary controls that satisfy the first and second laws of thermodynamics.
This paper proposes a novel adaptive algorithm for multi-robot multitask coverage that combines a federated control strategy with a multitask Gaussian Process framework to learn unknown sensory demands, achieving sublinear cumulative regret compared to an oracle with prior knowledge.
This paper extends irreversible port-Hamiltonian system formulations from one-dimensional to N-dimensional boundary-controlled distributed parameter systems, providing a unified, thermodynamically consistent framework for modeling conduction-diffusion phenomena that preserves energy balance and entropy production while enabling structure-preserving numerical control.
This paper proposes an enhanced Distributed Kalman-Consensus Filter for multi-object tracking in mobile robot networks that combines the MOTLEE framework's frame-alignment methodology with a novel adaptive uncertainty weighting mechanism to dynamically mitigate the impact of heterogeneous localization errors and communication latency, resulting in improved tracking accuracy.
This paper establishes conditions under which multi-stage Runge-Kutta methods preserve strong contractivity for infinitesimally contracting continuous-time systems, deriving coefficient-dependent criteria for explicit schemes and extending classical implicit guarantees to strong contractivity across , , and norms while ensuring unique solvability via an auxiliary dynamic system.
This paper proposes SliceFed, a novel Federated Constrained Multi-Agent Deep Reinforcement Learning framework that leverages a Lagrangian primal-dual approach with Proximal Policy Optimization to optimize dynamic spectrum slicing in 6G networks, achieving near-perfect URLLC latency compliance and robust interference management while preserving data privacy through federated learning.
To address the approximation errors in conventional nonlinear probabilistic latent variable models caused by amortized variational inference, this paper proposes KProxNPLVM, a novel framework that employs Wasserstein distance as a proximal operator to relax the learning objective, thereby sidestepping these errors and improving soft sensor prediction accuracy.
This paper proposes a low-complexity iterative algorithm for an ISAC-enabled multi-UAV collaborative target sensing scheme that jointly optimizes UAV-BS association, trajectories, and bandwidth allocation to minimize the posterior Cramer-Rao bound for tracking dynamic low-altitude targets while satisfying communication constraints.
This paper proposes a hybrid zonotope-based framework for computing exact and scalable forward and backward reachable sets of closed-loop ReLU RNNs without unrolling, featuring a tunable relaxation scheme to balance computational complexity and approximation accuracy while providing a sufficient condition for safety certification.
This paper proposes a multi-agent deep reinforcement learning framework utilizing virtual anchor nodes for unmanned aerial vehicles to achieve superior accuracy and efficiency in localizing chemical plume sources from undocumented orphaned wells compared to traditional fluxotaxis methods.
This paper proposes a novel rotatable antenna (RA)-enabled covert communication system that jointly optimizes transmit beamforming and RA rotational angles via an alternating optimization algorithm to maximize covert rate while satisfying covertness and power constraints, demonstrating superior performance over benchmark schemes.
This paper presents an optimized C++ implementation of the QPALM-OCP algorithm that exploits the inherent stage-wise parallelism in optimal control problems to significantly enhance computational performance through parallelization and vectorization.
This paper proposes a probabilistic forecasting framework using clustered quantile regression on UK low-voltage transformer data to dynamically optimize thermal protection settings, achieving a 10–12% capacity increase while enabling risk-informed operational decisions through direct overheating risk quantification.
This paper proposes an emergency-aware and frequency-constrained HVDC planning method for multi-area asynchronously interconnected grids that integrates a coordinated emergency control scheme and an enhanced system frequency response model to optimize inter-area HVDC capacities while balancing economic efficiency with frequency security requirements.
This paper proposes a robust parametric model predictive control framework for microgrid dispatch that addresses the endogenous uncertainty of battery degradation by integrating an XGBoost-based probabilistic degradation model with tunable penalty weights to optimize the trade-off between operational costs and long-term battery health under varying temperature conditions.
This paper presents an algebraic framework for approximating Markovian open quantum dynamics that guarantees complete positivity and trace preservation by projecting Lindblad generators onto their center manifolds, offering both asymptotically exact unitary reductions and perturbative methods with explicit error bounds while clarifying their connection to adiabatic elimination.