279 papers
This paper presents a hybrid learning and model-based approach that integrates multimodal tactile sensing with internal-force optimization to achieve reactive slip stabilization in multifingered grasps with a sub-50 ms closed-loop latency.
This paper extends the theory of Lorentzian and completely log-concave polynomials to proper convex cones by defining -Lorentzian forms and associated semipositive cones, establishing their geometric properties and negative-dependence interpretations, and applying these results to derive new Lyapunov stability criteria for cone-constrained evolution variational inequality systems.
This paper presents the design and experimental validation of a robust relative localization system for the SnailBot modular robot, which fuses ArUco marker recognition, optical flow, and IMU data to enable accurate real-time positioning for collaborative tasks.
This paper introduces Path-Consistent Safety Filtering (PACS), a novel approach that ensures formal safety guarantees for diffusion policies in dynamic environments while preserving task success rates by applying set-based reachability analysis to brake trajectories in a manner consistent with the policy's training distribution.
This paper proposes IMAS, a two-layer optimization algorithm that jointly selects sensing agents and synthesizes decentralized active perception policies in Dec-POMDPs by leveraging the submodularity of information-theoretic mutual information objectives to guarantee a -approximate performance.
This paper presents a hardware-based validation of angular droop control for grid-forming DC/AC converters, demonstrating its ability to unify primary and secondary control for exact frequency regulation while addressing implementation challenges like discretization and clock drift through single and multi-converter experiments.
This paper proposes and analyzes a finite-horizon approximation method for computing feedback Nash equilibria in infinite-horizon discrete-time linear-quadratic games, establishing conditions for equilibrium uniqueness, providing an efficient solution algorithm, and proving the convergence of costs with explicit error bounds.
This paper proposes a hybrid framework for autonomous rendezvous that utilizes an active learning-based input design to enhance observability for angle-only initial relative orbit determination, subsequently transitioning to an Extended Kalman Filter and Model Predictive Controller to achieve reliable end-to-end mission execution.
This paper proposes a distributed model predictive control framework that enables heterogeneous, nonlinear multi-agent systems to achieve dynamic cooperation and satisfy individual and coupling constraints by optimizing artificial references, thereby ensuring recursive feasibility, asymptotic stability, and emergent task solutions without predetermined trajectories.
This paper introduces Filtered Control Barrier Functions (FCBFs), a framework that integrates an input regularization filter with High-Order CBFs within a unified quadratic program to simultaneously guarantee system safety, control bounds, and Lipschitz continuity of control inputs, thereby preventing abrupt changes that could degrade performance or violate actuator limits.
This paper proposes a novel algorithm for verifying the safety of nonlinear neural feedback systems by computing tight polyhedral enclosures of their transition functions and encoding them into a mixed-integer linear program (MILP), achieving an order-of-magnitude improvement over existing state-of-the-art methods.
This paper proposes a robust control and gain scheduling approach that mitigates distributional shifts in approximant model parameters caused by system nonlinearity by constraining the closed-loop system to remain consistent with learning data, a problem formulated as a computationally efficient convex semi-definite program.
This paper introduces NanoBench, an open-source multi-task benchmark dataset collected on a 27g Crazyflie 2.1 nano-quadrotor that provides synchronized actuator-level signals, controller internals, and millimeter-accurate ground truth to address the unique modeling and control challenges of nano-scale aerial robotics.
This paper presents a lightweight 3D LiDAR-based UAV tracking system for small drones that utilizes an Adaptive Extended Kalman Filter to achieve robust, high-accuracy relative positioning in GPS-denied environments by dynamically adjusting to sparse and noisy point cloud data.
This paper proposes the RE-RPfair model, an extension of the Renewable Energy Robust Optimization Problem, which incorporates the Gini Index to ensure fair allocation of renewable energy suppression among PV sources while addressing uncertainties in unit commitment.
This paper proposes a distributed, formation-aware adaptive conformal prediction framework that generates risk-sensitive uncertainty bounds to enforce visibility constraints and ensure safe, high-performance leader-follower multi-robot operations under heteroscedastic perception errors.
This paper analyzes the solvability of parameter estimation-based observers (PEBO) for general nonlinear systems by examining the fundamental properties of transformability and identifiability, ultimately providing sufficient conditions to ensure their existence.
This paper presents a Safety Enhanced Passivity-Based Nonlinear Model Predictive Control (SEP-NMPC) framework that unifies strict passivity-based stability and high-order control barrier function safety guarantees to enable real-time, collision-free transport of slung payloads by quadrotors in cluttered environments.
This paper presents a novel hierarchical control framework that integrates an indirect adaptive law with model predictive control to enable quadrupedal robots to robustly transport heavy static and dynamic payloads across diverse terrains by estimating unknown parameters and ensuring stability through a convex stability criterion.
This paper reviews the evolving design, integration, and safety challenges of Emergency Locator Transmitters (ELTs) within More Electric Aircraft (MEA) environments, addressing critical constraints in power, thermal management, and electromagnetic compatibility while outlining future trends for enhanced reliability and certification.