The inverse dynamic games problem is to model expert demonstrations by identifying the underlying cost functions of multiple agents from observed trajectories of their dynamic game interactions. This article investigates discrete-time, finite-horizon linear-quadratic (LQ) problems where both the state weight matrix and input weight matrix are unknown, with the presence of both process noise and observation noise. In addition, each player’s cost function incorporates a player-specific, unknown linear term with respect to the state. Under this framework, first, sufficient conditions are established for the solvability of the weight matrices. Subsequently, it is proved that the inverse dynamic games problem involving heterogeneous unknown target states is structurally identifiable, unaffected by process noise. Building on the necessary conditions for Nash equilibrium solutions in forward problems, the estimation of the cost function parameters is formulated as a nontrivial solution to a homogeneous linear estimation problem, which can be implemented in a distributed manner. Furthermore, the proposed estimator achieves statistical consistency under the influence of observation noise. The effectiveness is illustrated through a multivehicle spring-coupled dynamic game and an interactive steering control scenario.

Learning based monocular SLAM systems such as MASt3R-SLAM achieve impressive dense reconstructions by leveraging learned priors, but suffer from non-physical deformations such as ghosting and structural misalignment due to inconsistent multi-frame fusion. In this work, we propose a globally consistent non-rigid map fusion framework that addresses these artifacts by introducing edge-guided deformation graphs into the SLAM pipeline. First, we restore metric scale by fusing predicted and measured depth maps via weighted least squares. Then, we extract geometrically meaningful control points using image-edge cues and match them across frames using MASt3R correspondences. These control points guide a deformation graph, where local non-rigid transformations are optimized via a combination of data fidelity, smoothness, and regularization losses. Finally, the deformation field is propagated to non-control points using Gaussian interpolation. Experiments on the Habitat simulator demonstrate that our method significantly reduces ghosting and improves reconstruction accuracy, outperforming existing methods both quantitatively and qualitatively.

The identification of heterogeneous nonlinear networks consisting of homogeneous clusters is investigated, which is challenging due to high computational complexity and partial state observations. To improve the computational efficiency, a finite-time horizon particle-based online expectation-maximization (EM) algorithm is proposed that enables distributed identification of unknown parameters across all agents even under complex agent couplings. To overcome the limitations caused by partial state observations, a neighbor-centered adapt-then-combine (ATC) strategy is developed for homogeneous clusters. This adaptive mechanism dynamically diffuses parameter estimates among neighboring agents, improving both accuracy and scalability for identifying large-scale networks. Theoretical analysis establishes the convergence of the proposed algorithm. Simulation examples validate the effectiveness and reliability of the proposed method, demonstrating its capability for a wide range of applications related to large-scale networks.

Sampling control trajectories from standard distributions—a foundation for Model Predictive Control (MPC) and Model Predictive Path Integral (MPPI) methods—although probabilistically complete, in practice leads to poor exploration of the configuration space, resulting in catastrophic outcomes in the robot’s operation. Recent works have proposed a family of approaches for sampling in control space that lead to uniform coverage of the configuration space, introducing the notion of C-Uniformity. However, those methods suffer from the need for state and action space discretization and level-sets, pre-computation. In this work, we introduce a proof-of-concept training strategy for deep generative models to achieve diverse and near C-Uniform sampling capabilities. Two variational inference approaches—information maximization algorithm and Stein variational gradient descent—are used as a foundation for training normalizing flow and flow matching models to sample control sequences that lead to a wider coverage of the configuration space, compared to the standard distributions, without state/action space discretization. Qualitative and quantitative evaluations support the advantage of the methods in terms of state space coverage and success rate in downstream social navigation tasks.

Existing studies on distributed filtering for nonlinear state-space models with state constraints have often overlooked communication limitations, which are essential for practical implementation. To investigate the interplay between state constraints and communication limitations, a projected distributed extended Kalman filter (DEKF) is proposed. The proposed framework employs an event-triggered (ET) mechanism to reduce the communication load, while filter gains are derived by minimizing an upper bound of the projection error covariance. The proposed method ensures the exponential asymptotic unbiasedness of the state estimation under relaxed assumptions. Furthermore, sufficient conditions are derived to guarantee the boundedness of the projection error covariance under the ET scheme, which further enables a triggered-threshold design. The effectiveness of the proposed method is validated through real-world ground vehicle formation experiments.

Wide field-of-view (FoV) LiDAR sensors provide dense geometry across large environments, but existing LiDAR-inertial-visual odometry (LIVO) systems generally rely on a single camera, limiting their ability to fully exploit LiDAR-derived depth for photometric alignment and scene colorization. We present Omni-LIVO, a tightly coupled multi-camera LIVO system that leverages multi-view observations to comprehensively utilize LiDAR geometric information across extended spatial regions. Omni-LIVO introduces a Cross-View direct alignment strategy that maintains photometric consistency across non-overlapping views, and extends the Error-State Iterated Kalman Filter (ESIKF) with multi-view updates and adaptive covariance. The system is evaluated on public benchmarks and our custom dataset, showing improved accuracy and robustness over state-of-the-art LIVO, LIO, and visual-inertial SLAM baselines.

Multi-object tracking (MOT) aims to detect targets continuously while preserving identity consistency and estimating their dynamic states. Conventional approaches typically employ the Kalman Filter (KF) and its variants as the core state estimator, yet their performance heavily depends on precise motion modeling and manual noise tuning, which often deteriorates in complex or sensor-impaired environments. To address these limitations, we propose a 3D MOT framework built upon a Neural Prediction-Update (NPU) module. The NPU replaces the KF's prediction step with a lightweight neural network and adopts an observation-centric update strategy, thereby eliminating the need for explicit motion models and improving robustness against uncertain dynamics. Furthermore, to extend our framework to scenarios with only 2D detections, we propose a Projection-guided Physical-constraint-based Matching (PPM) method that searches for 3D states whose projections best match the detected 2D boxes. By enforcing geometric and physical consistency, PPM generates reliable 3D observations, improving spatial estimation under partial sensing conditions. Extensive experiments on the KITTI dataset demonstrate that our framework achieves superior accuracy and robustness compared with existing 3D MOT methods.

This article develops a scheme to tackle the safe optimal formation tracking issue for multiple fixed-wing uncrewed aerial vehicles (UAVs) with external disturbances and asymmetric control constraints. To ensure safety constraints in collision avoidance, a safe set is first constructed by a super level set of a continuously differential function, following a novel control barrier function (CBF) to characterize the safety. Subsequently, we transform the safe optimal formation tracking control into a constrained zero-sum (ZS) differential game to mitigate the destabilizing effects of the disturbances, where the cost function is constructed in a nonquadratic form to cope with asymmetric input constraints. Particularly, the designed CBF is integrated into the cost function to penalize the unsafe behavior, and a damping coefficient is included to balance the optimality and safety. Afterwords, a critic-only reinforcement learning (RL) strategy is developed to learn the robust safe Nash policy, where the critic weights are updated by applying experience replay technology, thus avoiding the requirement for persistence of excitation condition. Moreover, the stability and forward invariance of the safe set of the presented scheme are also verified. Finally, simulation examples are provided to substantiate the validity of the control scheme.

This paper presents a scheme to tackle the fixed-time (FxT) safe optimal obstacle avoidance control issue of nonlinear systems in the presence of external disturbances and multiple obstacles. To mitigate the destabilizing effects of the disturbances, a zero-sum differential game is first formulated, where the safety controller endeavors to minimize the performance index, whereas the disturbance attempts to maximize it. The subsequent development integrates a barrier function (BF) associated with obstacles into the cost function, ensuring the system's safety. Particularly, a damping constant is incorporated to achieve a balance between safety and optimality. By establishing the forward invariance of the safe set and demonstrating the FxT stability of the closed-loop system, a sufficient condition that characterize the FxT safe Nash equilibrium point is provided for the first time, where the Lyapunov function satisfying the FxT convergence differential inequality is also the solution to the steady-state Hamilton-Jacobi-Isaacs (HJI) equation guaranteeing optimality. Afterwards, a critic-only reinforcement learning (RL) strategy is developed and rigorously verified for learning the safe Nash policy within a fixed time. Moreover, the paper proves the FxT stability of the closed-loop system when operating under the approximate optimal Nash strategy. Finally, two simulation scenarios are presented to substantiate the validity of the proposed control framework. (c) 2025 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

This paper addresses the inverse optimal control for the linear quadratic tracking problem with a fixed but unknown target state, which aims to estimate the possible triplets comprising the target state, the state weight matrix, and the input weight matrix from observed optimal control input and the corresponding state trajectories. Sufficient conditions have been provided for the unique determination of both the linear quadratic cost function as well as the target state. A computationally efficient and numerically reliable parameter identification algorithm is proposed by equating optimal control strategies with a system of linear equations, and the associated relative error upper bound is derived in terms of data volume and signal-to-noise ratio. Moreover, the proposed inverse optimal control algorithm is applied for the joint cluster coordination and intent identification of a multi-agent system. By incorporating the structural constraint of the Laplace matrix, the relative error upper bound can be reduced accordingly. Finally, the algorithm's efficiency and accuracy are validated by a vehicle-on-a-lever example and a multi-agent formation control example.

Existing research on distributed fault detection (DFD) rarely considers coupled state constraints, which are common in multi-agent systems (MASs). To fill this gap, this paper investigates the problem of DFD for MASs with coupled state constraints. By employing the finite-horizon H∞ filtering framework and the augmented Lagrange method, a distributed fault detector is developed to cope with coupled state constraints and normbounded noises using the relative output information among neighbors. Based on the sufficient convergence conditions of local iterative solutions, the covariance correction is derived, and mild conditions are proposed to guarantee the finite-time boundedness of the local error covariance. Finally, practical experimental results demonstrate that the proposed algorithm effectively reduces cumulative estimation errors and fault detection delays while maintaining strong robustness against uncertainties.

Inferring the underlying topology of complex dynamical networks is a fundamental problem in network science and system identification. In practical applications, topology inference is challenging due to unknown system dynamics, measurement noise, and partial state observability. To address these challenges, we propose a data-driven topology identification framework based on Koopman operator theory, delay embedding, and neural-network-based nonlinear representation learning. The Koopman dictionary functions are learned adaptively using neural networks, enabling flexible lifted representations directly from observed data. The proposed approach supports topology inference under partial state observability and demonstrates robust performance in the presence of measurement noise. Experimental results on both synthetic and realistic benchmark datasets show that the proposed approach can effectively recover network interaction structures while simultaneously learning a structured lifted dynamical representation of the underlying system.

Range-only simultaneous localization and mapping (RO-SLAM) is crucial for mobile robot navigation. However, the lack of directional information in distance measurements causes significant nonlinearity and convergence issues. These challenges are particularly prominent during beacon initialization. During this phase, joint estimation of the robot pose and beacon positions becomes difficult. To tackle this problem, we propose a novel initialization algorithm using particle filtering. This algorithm models beacon position uncertainty using von Mises-Fisher (vMF) distributions. It also introduces an innovative resampling strategy with dynamically adjusted particle numbers. Building on this initialization, we construct a comprehensive 3-D RO-SLAM framework. Both simulation and real-world experiments show that the proposed algorithm provides accurate and efficient estimation of robot pose and beacon positions. These results demonstrate improvements in both initialization accuracy and speed.

We design specific neural networks (NNs) for the identification of switching nonlinear systems in the state-space form, which explicitly model the switching behavior and address the inherent coupling between system parameters and switching modes. This coupling is specifically addressed by leveraging the expectation-maximization (EM) framework. In particular, our technique will combine a moving window approach in the E-step to efficiently estimate the switching sequence, together with an extended Kalman filter (EKF) in the M-step to train the NNs with a quadratic convergence rate. Extensive numerical simulations, involving both academic examples and a battery charge management system case study, illustrate that our technique outperforms available ones in terms of parameter estimation accuracy, model fitting, and switching sequence identification.

Distributed parameter identification for large-scale multi-agent networks encounters challenges due to nonlinear dynamics and partial observations. Simultaneously, ensuring the stability is crucial for the robust identification of dynamic networks, especially under data and model uncertainties. To handle these challenges, this paper proposes a particle consensus-based expectation maximization (EM) algorithm. The E-step proposes a distributed particle filtering approach, using local observations from agents to yield global consensus state estimates. The M-step constructs a likelihood function with an a priori contraction-stabilization constraint for the parameter estimation of isomorphic agents. Performance analysis and simulation results of the proposed method confirm its effectiveness in identifying parameters for stable nonlinear networks.

We consider the identification of non-causal systems with random switching modes (NCSRSM), a class of models essential for describing typical power load management and department store inventory dynamics. The simultaneous identification of causal-andanticausal subsystems, along with the presence of random switching sequences, however, make the overall identification problem particularly challenging. To this end, we develop an expectation-maximization (EM) based system identification technique, where the E-step proposes a modified Kalman filter (KF) to estimate the states and switching sequences of causal-and-anticausal subsystems, while the M-step consists in a switching least-squares algorithm to estimate the parameters of individual subsystems. We establish the main convergence features of the proposed identification procedure, also providing bounds on the parameter estimation errors under mild conditions. Finally, the effectiveness of our identification method is validated through two numerical simulations.

The primary challenge in 3D range-only SLAM is achieving rapid and accurate state estimation in high-dimensional environments. This paper introduces a distributed adaptive sub-filter framework, in which independent sub-filters are designed for each beacon. Within each sub-filter, a hybrid Unscented Kalman Filter (UKF) and particle filter(PF) approach is used for local state estimation. The global filter output is then derived by fusing the estimation results from all individual sub-filters. Simulation experiments show that, under identical experimental conditions, the proposed method outperforms the distributed UKF and distributed UIKF in terms of estimation accuracy.

>Dear Editor,This letter studies output consensus problem of heterogeneous linear multiagent systems over directed graphs. A novel adaptive dynamic event-triggered controller is presented based only on the feedback combination of the agent's own state and neighbors' output,which can achieve exponential output consensus through intermittent communication. The controller is obtained by solving two linear matrix equations, and Zeno behavior is excluded.

This paper proposes an approach for fixed-time (FxT) adaptive optimized formation control of nonlinear multi-agent systems (MASs) with unknown nonlinear dynamics and full-state constraints. To address system uncertainty and state constraints while achieving optimality in FxT settings, the paper presents a novel adaptive estimation and analysis. The proposed approach first introduces a tan-type nonlinear mapping to handle state constraints, eliminating the feasibility condition of the conventional barrier Lyapunov function method. Next, the actual optimal controller is iteratively designed using the identifier-actor-critic structure and optimized backstepping method, with neural approximators used to learn system uncertainty. Finally, a monotonically decreasing function is constructed to prove that the designed actor-critic update laws have an upper bound, which is essential for stability analysis. The proposed scheme can ensure that the formation is realized at a fixed time while optimizing a given performance index and meeting the constraint requirements. The simulation results verify the effectiveness of the proposed approach.