284 papers
This paper introduces a task-relative taxonomy of actuator inputs in feedback-linearizable nonlinear systems, defining "dexterity" inputs that can be deactivated to gracefully downgrade tasks while preserving linearization on shared outputs through a unified controller based on dynamic prolongation.
This paper introduces Drag-Aware Aerodynamic Manipulability (DAAM), a geometric framework for control allocation in redundant multirotors that utilizes a Riemannian metric to explicitly account for motor torque limits and aerodynamic drag, thereby generating a state-dependent manipulability volume that serves as a natural barrier function to optimize redundancy resolution while characterizing the resulting smooth manifolds and global jump discontinuities.
This paper addresses the robust cooperative output regulation of discrete-time uncertain heterogeneous multi-agent systems by establishing global and local sufficient conditions for the existence and design of structured control gains, utilizing structured Lyapunov inequalities to derive linear matrix inequalities (LMIs) that enable both centralized and distributed controller synthesis.
This paper proposes a convex, delay-dependent output-feedback control synthesis method for LPV systems with time-varying input delays by integrating parameter-dependent Lyapunov functions with dynamic IQC multipliers and an exact-memory controller structure, thereby overcoming the non-convexity of memoryless designs to achieve reduced conservatism and improved performance.
This paper introduces the novel concept of two-storage strict dissipativity to bridge the gap between storage functions and value functions in economic model predictive control, proving it as a necessary and sufficient condition for asymptotic stability while offering easier verification and new terminal cost designs.
This paper proposes a model-free predictive tracking algorithm for unknown nonlinear systems with Koopman linear embeddings that leverages Willems' fundamental lemma to achieve dynamic regret decaying exponentially with the prediction horizon, effectively bridging the performance gap between nonlinear and lifted linear counterparts.
This paper proposes a tractable two-layer framework combining a Hidden Markov Model for inferring rival energy states and a Deep Q-Network for decision-making to optimize 2026 Formula 1 energy strategies under partial observability, specifically addressing the "counter-harvest trap" where opponents deliberately mask their deployment signals.
This paper proposes ORN-CBF, a hypernetwork-based learning framework that utilizes Hamilton-Jacobi reachability analysis to generate observation-conditioned neural control barrier functions, ensuring rigorous safety guarantees and improved generalization in partially observable environments through simulation and hardware experiments.
This paper introduces a novel Coupled Oscillator Network (CON) model that overcomes key limitations in latent-space control by ensuring Lagrangian structure, global input-to-state stability, and an invertible input-force mapping, thereby enabling efficient closed-form control strategies for complex mechanical systems using only raw visual feedback.
This paper presents an inverse dynamic game algorithm that uses mixed-integer linear programs to learn parametric constraints from multi-agent interaction demonstrations by encoding Karush-Kuhn-Tucker conditions, thereby providing theoretical guarantees for recovering inner approximations of safe and unsafe sets to enable robust motion planning.
This paper demonstrates that frozen transformers, when used in an in-context learning setting, can implicitly infer hidden states to accurately predict the outputs of both linear and nonlinear dynamical systems from noisy observations, achieving performance comparable to optimal and heuristic Bayesian filters without requiring test-time gradient updates or explicit knowledge of the system model.
This paper establishes finite-sample guarantees for a cost-driven representation learning method that learns a latent dynamic model by predicting multi-step costs, enabling the derivation of near-optimal controllers for finite-horizon Linear Quadratic Gaussian (LQG) control problems without explicitly modeling observations or actions.
This paper proposes a model-based reinforcement learning framework that integrates Lagrangian neural networks into the Dyna architecture to enforce physical laws and improve prediction accuracy, demonstrating that state-estimation-based optimization converges faster than stochastic gradient-based methods during training.
This paper presents an adaptive entropy-driven sensor selection policy within a camera-LiDAR particle filter that dynamically switches between modalities to optimize tracking accuracy and continuity for single-vessel surveillance, validated through real-world maritime deployment.
This paper introduces IronEngine, a general AI assistant platform featuring a unified orchestration core and a three-phase pipeline that integrates diverse backends, adaptive memory, and extensive tooling to achieve high task completion rates while separating planning quality from execution capability.
This paper presents an end-to-end, viewpoint-agnostic grasping pipeline for mobile legged manipulators that leverages vision-language models and partial observation compensation to achieve robust, language-guided object selection and safe execution in cluttered environments, outperforming view-dependent baselines with a 90% success rate.
This paper proposes a lightweight, fully online Model Predictive Control framework for brand auction ads that utilizes isotonic regression to build monotonic bid models from streaming data, thereby improving spend efficiency and cost control without the need for complex machine learning models.
This paper introduces a distribution-free framework for robust trajectory optimization of non-Gaussian stochastic systems that leverages conformal inference and contraction theory to generate finite-sample, statistically valid chance-constraint guarantees without requiring structural priors.
This paper proposes a topology-aware graph reinforcement learning framework that integrates persistence homology to enhance power distribution network resilience, demonstrating superior performance in maximizing power delivery and minimizing voltage violations across diverse outage scenarios compared to baseline models.
This paper proposes a SISA-based machine unlearning framework that efficiently localizes power transformer inter-turn short-circuit faults by isolating and selectively retraining only the data shards affected by sensor poisoning, thereby achieving diagnostic accuracy comparable to full retraining while significantly reducing computational time.