322 papers
This paper introduces CBF-RL, a framework that integrates Control Barrier Functions directly into the reinforcement learning training process to internalize safety constraints, thereby enabling safe, robust, and filter-free deployment of policies in real-world scenarios like humanoid robot navigation.
This paper demonstrates that existing alignment-based defenses against control-flow hijacking in multi-agent systems are vulnerable to evasion due to inherent safety-functionality conflicts and limited context visibility, and proposes ControlValve, a new defense mechanism that enforces control-flow integrity and least privilege through permitted control-flow graphs and contextual rules.
This paper introduces TEMPO-VINE, a comprehensive multi-temporal, multi-modal public dataset featuring heterogeneous sensors and ground truth trajectories across diverse vineyard conditions, designed to address the lack of realistic benchmarks for advancing autonomous localization, mapping, and place recognition in precision agriculture.
This paper presents a robust bimorph lithium niobate PMUT utilizing a 20 μm thick periodically poled film that achieves a high electromechanical coupling of 6.4% and demonstrates exceptional thermal resilience with stable operation up to 600°C and survival up to 900°C.
This paper presents a computationally efficient mollification-based method that regularizes non-differentiable planning outputs into smooth, executable paths with guaranteed curvature bounds, enabling real-time implementation on microcontrollers while satisfying the strict differentiability requirements of nonholonomic robot control.
This paper presents an efficient algorithm that utilizes partial Gröbner basis computation and sparse interpolation to find simple generating sets for subfields of rational function fields, demonstrating improved performance and practical utility in applications like structural parameter identifiability.
This paper proposes an infinite-dimensional closed-loop inverse kinematics framework for underactuated soft robots that leverages differentiable neural operators to learn actuation-to-shape mappings, enabling efficient task-space control by reasoning about the robot's entire continuous shape rather than finite configurations.
This paper proposes an optimal real-time data fusion framework under Rényi differential privacy that jointly optimizes the fusion policy and state estimation to minimize error while adaptively allocating a privacy budget, validated through a traffic density estimation case study.
This paper proposes a System-of-Systems convergence paradigm grounded in a meta-cognition map and SysML to overcome disciplinary silos and ontological barriers, demonstrating its effectiveness through a multi-domain case study of the Chesapeake Bay Watershed to support integrated decision-making for complex societal challenges.
This paper proposes a Dual-Interaction-Aware Cooperative Control (DIACC) strategy based on Multi-Agent Reinforcement Learning, which integrates decentralized decision-making, centralized value estimation, and a novel reward design to effectively alleviate mixed traffic congestion by distinguishing between cooperative and observational vehicle interactions.
This paper introduces AMV-L, a value-driven memory lifecycle framework for long-running LLM agents that replaces age-based retention with utility-based tiering to bound retrieval workloads, thereby achieving significantly improved tail-latency control and throughput compared to traditional TTL and LRU policies.
This paper introduces a risk-aware formalism for evaluating system trajectories under uncertainty by explicitly modeling system-environment interactions and hierarchical objectives, thereby establishing a consistent preference ordering and enhancing explainability in autonomous driving scenarios.
This paper proposes and validates a joint Visible Light Communication and Ambient Backscatter architecture for batteryless Ambient IoT networks, detailing its fundamental concepts, diverse applications, and experimental proof-of-concept demonstrations while outlining future research directions and deployment challenges.
This paper establishes explicit conditions for a nonlinear proportional-integral controller to guarantee the boundedness and robustness of all solutions in a three-state Moore-Greitzer compressor model, utilizing a non-standard circle-criterion analysis to address the system's unstabilizable linearization and sector-bounded nonlinearity.
This paper proposes a rare event-aware control method for power systems that utilizes a Fleming-Viot particle approach within a multistage stochastic programming framework to generate biased scenarios of prolonged renewable energy shortfalls, thereby enabling cost-effective and robust ramping of conventional power plants.
This study demonstrates that adaptive policy switching for two-wheeled differential robots traversing diverse lunar lava tube terrains can be effectively achieved by classifying terrain types with over 98% accuracy using short-term pitch data and Gaussian mixture models.
This paper proposes and validates a data-driven control framework for magnetically actuated fish-like robots that combines a neural network-based forward dynamics model with gradient-based model predictive control and imitation learning to achieve precise path following without relying on analytical modeling.
This paper establishes that mixed H2/H-infinity control exhibits benign nonconvexity where every stationary point is globally optimal, by characterizing the feasible set's topology and employing an Extended Convex Lifting framework to bridge nonconvex policy optimization with convex reformulations for scalable, data-driven applications.
This paper proposes adopting Height Above Ellipsoid (HAE) as a unified, GNSS-native vertical reference to resolve the safety and interoperability challenges caused by fragmented altitude systems in the low-altitude economy, demonstrating through real-world implementation and risk assessment that this transition enhances dynamic airspace capacity while maintaining safety standards.
This paper introduces U-OBCA, an uncertainty-aware optimization-based collision avoidance framework that utilizes Wasserstein distributionally robust chance constraints to handle polygon-shaped robots and obstacles without geometric simplification, thereby significantly reducing conservatism and improving navigation efficiency in narrow, cluttered environments compared to existing methods.