In large language model-based agents, memory serves as a critical capability for achieving personalization by storing and utilizing users' information. Although some previous studies have adopted memory to implement user personalization, they typically focus on preference alignment and simple question-answering. However, in the real world, complex tasks often require multi-hop reasoning on a large amount of user information, which poses significant challenges for current memory approaches. To address this limitation, we propose the multi-hop personalized reasoning task to explore how different memory mechanisms perform in multi-hop reasoning over personalized information. We explicitly define this task and construct a dataset along with a unified evaluation framework. Then, we implement various explicit and implicit memory methods and conduct comprehensive experiments. We evaluate their performance on this task from multiple perspectives and analyze their strengths and weaknesses. Besides, we explore hybrid approaches that combine both paradigms and propose the HybridMem method to address their limitations. We demonstrate the effectiveness of our proposed model through extensive experiments. To benefit the research community, we release this project at https://github.com/nuster1128/MPR.

In transportation systems, accurately estimating the remaining useful life (RUL) of critical components, such as aircraft engines, Battery Management Systems (BMSs), is crucial for the safe and reliable operation and manufacturing of transportation vehicles. However, most existing research overlooks the underlying physical information, which is vital for more precise RUL prediction. To fill this gap, this paper proposes a physics-informed method for predicting the RUL of key components of transportation vehicles. By integrating the Mamba network with a multi-head attention mechanism, we capture and emphasize key features and trends in the equipment’s operational state, improving prediction accuracy. Additionally, we introduce a Physics-Informed Neural Network (PINN) framework to model the underlying physical relationships between RUL and sensor data, incorporating these relationships as a regularization term in the loss function to enhance predictive capability and interpretability. We conducted experimental validation using the C-MAPSS aircraft engine dataset (operation) and the transportation vehicle chip manufacturing dataset (manufacture). The results show that the proposed method significantly improves the accuracy of RUL prediction, providing strong support for the intelligent maintenance and reliability management of key components in transportation vehicles.

LLMs have emerged as powerful evaluators in the LLM-as-a-Judge paradigm, offering significant efficiency and flexibility compared to human judgments. However, previous methods primarily rely on single-point evaluations, overlooking the inherent diversity and uncertainty in human evaluations. This approach leads to information loss and decreases the reliability of evaluations. To address this limitation, we propose a novel training framework that explicitly aligns the LLM-generated judgment distribution with human evaluation distributions. Specifically, we propose a distributional alignment objective based on KL divergence, combined with an auxiliary cross-entropy regularization to stabilize the training process. Furthermore, due to limited human annotations, empirical human distributions are merely noisy estimates of the true underlying distribution. We therefore incorporate adversarial training to ensure a robust alignment with this true distribution, rather than overfitting to its imperfect approximation. Extensive experiments across various LLM backbones and evaluation tasks demonstrate that our framework significantly outperforms existing closed-source LLMs and conventional single-point alignment methods, with superior alignment quality, strong robustness, and competitive evaluation accuracy.

In this paper, the leader-follower fixed-time consensus problem of networked nonlinear multi-agent systems (NNMASs) with unknown disturbance is investigated. A new event-triggered super-twisting fixed-time sliding mode consensus control (ESFSMCC) method is proposed, enabling all agents to achieve consensus in a fixed time. Firstly, a terminal sliding mode variable is designed to eliminate the convergence time dependence on the initial values of the system and avoid singularity. Secondly, a distributed event-triggered mechanism is developed to effectively reduce inter-agent communication frequency in a networked environment. In contrast to the existing fixed-time consensus control method, the improved super-twisting algorithm is adopted to construct a control protocol to achieve both global fixed-time consensus robustness and alleviate the issue of high-frequency chattering. Thirdly, the Lyapunov theory is utilized without the piecewise sliding mode technique to derive sufficient conditions for establishing the fixed-time stability of NNMASs, which still avoids the singularity problem. In this paper, the non-segmented terminal sliding mode is employed to prove the global fixed-time stability of the system states, thereby avoiding computational complexity. Finally, the effectiveness and advantages of the proposed method are verified through numerical simulations. Note to Practitioners—With the flourishing development of networks today, networked control is poised to become a future research hotspot, especially when large-scale agents interact and collaborate. The efficient utilization of network resources has become increasingly urgent due to the proliferation of such interactions. Consequently, reducing energy consumption poses a significant challenge. Moreover, the high-frequency chattering of control inputs presents a hindrance to the practical application of SMC. To address these issue, this paper proposes an event-triggered super-twisting distributed control protocol for NNMASs. This protocol not only eliminates the influence of initial values on stability time, thereby enhancing its practical value, but also mitigates the impact of input chattering, providing robust support for practical applications. Finally, a dynamics model of a multi-robotic manipulator is employed to verify the correctness and effectiveness of the proposed method. Additionally, underwater autonomous vehicle formations are expected to become valuable tools for future ocean resource exploration, while drone formations will likely become the preferred choice for agricultural development, geological exploration, and transportation. Even space exploration will involve coordinated interaction between multiple spacecraft. These are typical applications of NNMASs.

Existing cooperative manipulation methods for multiple manipulator systems usually assume that the grasp matrix and the desired trajectory of each manipulator are known in advance. In this work, distributed neural adaptive impedance control (AIC) strategies integrating fully distributed observers are proposed to remove both limitations. Specifically, two fully distributed finite-time observers are designed to estimate the actual and ideal states of the reference point without using global information. The estimates of the grasp matrix and the desired trajectory of each end-effector (EE) are then obtained by kinematic constraints and the estimates of the reference point's states. At the controller development, a distributed adaptive impedance model is established to achieve an adaptive trade-off between tracking performance and compliance. Then, distributed neural network (NN)-based tracking control strategies are developed to asymptotically realize the desired adaptive impedance dynamics in the presence of uncertainties. Additionally, a virtual energy tank (EK) is employed to interact with the impedance system to correct the adaptive impedance laws for system passivity. A simulation for four mobile manipulators tightly cooperative transport an unknown object is carried out to demonstrate the established results.

Free-form surfaces have been widely used in industrial design and manufacturing. For the requirements of measurement efficiency and precision, robots and optical scanners are applied to measure free-form surface parts increasingly. Due to the complex geometry shapes and occlusions of these parts, how to plan accessible viewpoints of a scanner to achieve the expected coverage rate is a challenging task. This paper presents a novel viewpoint planning method based on the visibility cone space explorer (VP-VCSE) for robotic measurement systems with 7 degrees of freedom (7-DOF). A digital twin for the robotic measurement system is implemented to provide core services for robotic measurement tasks, including sensor simulation and collision detection. To generate initial candidate viewpoints, a novel mesh segmentation algorithm based on the hybrid mixture model is proposed, which is convenient to handle the triangular mesh of the target object. Visibility computation for a target object in given viewpoints is the key to dealing with the occlusion problem. For this purpose, a general visibility model of a structured-light scanner is presented to compute visible areas accurately. In order to reduce occlusions, a visibility cone space explorer is designed to search optimal candidate viewpoints considering inverse kinematics and physical collisions simultaneously. The viewpoint planning problem is formulated as a set covering optimization problem and a next-best-view operator is introduced to improve the efficiency of the genetic algorithm for searching the resultant viewpoint set, guaranteeing the expected coverage rate and data overlap rate. The simulation and experiment results for four different test models show that the proposed algorithm outperforms the existing methods in terms of the uncovered rate and the minimum number of viewpoints.Note to Practitioners—This paper addressed a viewpoint planning problem for the robotic measurement system with a binocular structured light 3D scanner mounted on the end effector of the robot, where a robot and a turntable cooperate to complete the measurement tasks. The goal is to find a minimal number of viewpoints that provides full coverage of the target surfaces. Although many studies have addressed this problem, there is little discussion about strategies to improve coverage rate when the target object has complex occlusions. This paper suggested a valuable practice to construct a visibility cone space to adjust viewpoint to reduce occlusions and improve the overall coverage rate. Simulation and experimental results demonstrated the feasibility and effectiveness of the proposed approach. This paper showed how to deal with various constraints that a feasible viewpoint needs to satisfy in the viewpoint generation, viewpoint adjustment, and viewpoint selection phase. Moreover, this paper provided a solution for developing the visualization, simulation, and interaction of a digital twin for the 7-DOF robotic measurement system. All core services for robotic measurement tasks are implemented based on a set of open source libraries, which provides a convenient learning and research software platform for practitioners. In future research, we will study how to improve the intelligence and cooperation of the robotic measurement system through deep learning or reinforcement learning techniques.

In this paper, considering the difficulty of the interaction between the multi-mobile manipulators and the environment, the dynamics model of the mobile manipulator is analyzed, and a hybrid force/position control method based on the prescribed performance is proposed to improve the stability of the multi-mobile manipulators in the process of cooperative object transportation. Firstly, the dynamics model of the underdriven system of the multi-mobile manipulators is established by the Newton-Euler theorem. Then, the equivalent control theory is adopted for underdriven system, and a prescribed performance control method is proposed by considering the motion interference between the mobile manipulator and the high precision control of the manipulator. At the same time, an adaptive impedance control method is used to overcome internal and external disturbance during the cooperative transport of multi-mobile manipulators. The stability of the proposed method is analyzed through the Lyapunov stability theory. Finally, the effectiveness and superiority of the proposed scheme are verified through a simulation of multi-mobile manipulators collaborative object transportation.

This article aims to solve the problem of formation control of mobile robots based on image and provide a low-cost as well as ease-of-implementation solution for mobile robots relying merely on a monocular camera under field-of-view (FOV) constraints. A low-complexity image-based visual servo controller is proposed, which can achieve the desired relative position on the image plane and solve the FOV constraints without the feature depth and leader’s velocities information. To facilitate the control design, a state transformation is first performed to decouple the visual motion kinematics. Then, an error transformation is introduced to handle the FOV constraints, and performance specifications are incorporated in the error transformation to achieve the predefined control performance. Finally, a simple static controller is derived using only information from images, and the stability of the uncertain system with unknown control direction/coefficients under the given performance control condition is analyzed. The effectiveness and performance of the proposed visual servoing controller can be illustrated using both simulations and experiments.

This article studies the group coordinated control problem for distributed nonlinear multiagent systems (MASs) with unknown dynamics. Cloud computing systems are employed to divide agents into groups and establish networked distributed multigroup-agent systems (ND-MGASs). To achieve the coordination of all agents and actively compensate for communication network delays, a novel networked model-free adaptive predictive control (NMFAPC) strategy combining networked predictive control theory with model-free adaptive control method is proposed. In the NMFAPC strategy, each nonlinear agent is described as a time-varying data model, which only relies on the system measurement data for adaptive learning. To analyze the system performance, a simultaneous analysis method for stability and consensus of ND-MGASs is presented. Finally, the effectiveness and practicability of the proposed NMFAPC strategy are verified by numerical simulations and experimental examples. The achievement also provides a solution for the coordination of large-scale nonlinear MASs.