Stateful P4 network functions introduce operational failures that emerge only under temporally ordered packet sequences and control-plane states. Existing temporal verifiers (e.g., P4TV) stop at logical verdicts, while dynamic testers (P4Testgen, CHIMERA) execute packets without temporal specifications, and both require operators to hand-author formal specifications. We present IntentP4, a formal-methods-aided pipeline that closes this loop: it translates an operator's natural-language intent into a P4LTL specification and then into a replayable multi-packet test case (packet sequence, control-plane rules, external operations, oracles), grounded throughout in compiler artifacts via a tool-queryable ProgramContext and gated by deterministic per-stage validators. On five stateful P4 programs spanning access control, monitoring, heavy-hitter detection, failure recovery, and load balancing, the P4LTL-to-test generator produces 10 scenarios, 89 packets, and 110 unified execution operations that pass eight consistency checks; on 11 specification subtasks, 4 strictly pass and 3 are semantically close; and an integrated BMv2/Mininet loop exposes runtime failures including a firewall policy-bypass manifestation and a missing multi-table control-plane entry under controller convergence.

Autonomous mobile robots (AMRs) equipped with high-quality cameras are revolutionizing the field of autonomous photography by delivering efficient and cost-effective methods for capturing dynamic visual content. As AMRs are deployed in increasingly diverse environments, the challenge of consistently producing high-quality photographic content remains. Traditional approaches often involve AMRs following a predetermined path while capturing data-intensive imagery, which can be suboptimal, especially in environments with limited connectivity or physical obstructions. These drawbacks necessitate intelligent decision-making to pinpoint optimal vantage points for image capture. Inspired by Next Best View studies, we propose a novel autonomous photography framework that enhances image quality and minimizes the number of photos needed. This framework incorporates a proposed evaluation metric that leverages ray-tracing and Gaussian process inter-polation, enabling the assessment of potential visual information from the target in partially known environments. A derivative-free optimization (DFO) method is then proposed to sample candidate views and identify the optimal viewpoint. The effectiveness of our approach is demonstrated by comparing it with existing methods and further validated through simulations and experiments with various vehicles. Note–Code and videos of the simulations and experiments are provided in the supplementary material and can be accessed at https://www.bezzorobotics.com/sg-lb-icra25.

Cellular Internet card (IC) as a new business model emerges, which penetrates rapidly and holds the potential to foster a great business market. However, with the explosive growth of IC users, the user churn problem becomes severe, affecting the IC business significantly, while there is lacking appropriate techniques in the literature to deal with the issue. In this article, we take the lead to study one large-scale data set from a provincial network operator of China, which contains about 4 million IC users and 22 million traditional card (TC) users. We first justify the IC user churn issue with data, and categorize the user churning reasons. Then, we shed light on understanding user portraits, which is the building block to enable efficient model design. Particularly, we conduct a systematical analytics on usage data by studying the difference of two types of users, examining the impact of user properties, and characterizing the user Internet using behaviors. Finally, by using the IC user portraits and usage patterns, we propose anICuserChurnPrediction model, namedICCP, which consists of a feature extraction component and a learning-based churn prediction architecture design. For feature extraction, both the static portrait features and temporal sequential features are captured. In the learning architecture, we devise the principal component analysis (PCA) block and the embedding/transformer layers to learn the respective information of two types of features, which are collectively fed into the classification multilayer perceptron layer (MPL) for churn prediction. A reference implementation ofICCPis conducted within the telecom system and extensive experiments corroborate the efficiency ofICCP.

In communication restricted environments, a multi-robot system can be deployed to either: i) maintain constant communication but potentially sacrifice operational efficiency due to proximity constraints or ii) allow disconnections to increase environmental coverage efficiency, challenges on how, when, and where to reconnect (rendezvous problem). In this work we tackle the latter problem and notice that most state-of-the-art methods assume that robots will be able to execute a predetermined plan; however system failures and changes in environmental conditions can cause the robots to deviate from the plan with cascading effects across the multi-robot system. This paper proposes a coordinated epistemic prediction and planning framework to achieve consensus without communicating for exploration and coverage, task discovery and completion, and rendezvous applications. Dynamic epistemic logic is the principal component implemented to allow robots to propagate belief states and empathize with other agents. Propagation of belief states and subsequent coverage of the environment is achieved via a frontier-based method within an artificial physics-based framework. The proposed framework is validated with both simulations and experiments with unmanned ground vehicles in various cluttered environments.

Cooperative multirobot systems coordinate their motion by exchanging information through consensus schemes to achieve a common goal. In the event of stealthy cyber attacks, compromised measurements and communication broadcasts can hijack a portion or the entire system toward undesired states. However, in order for these attacks to be effective, they have to exhibit nonrandom characteristics that contradict the expected multirobot system behavior. To deal with these hidden attacks, we propose a runtime monitoring framework that considers the signedresidual, defined as the difference between the expected and the received information to identify and isolate unexpected nonrandom behavior within the multirobot system. Specifically, the technique that we propose—namedCumulative Signdetector—monitors and compares changes in signed values of residual with their expected occurrences to detect inconsistencies and trigger alarms when an attack is discovered. Our results are validated theoretically by providing detection bounds and are demonstrated with simulations and experiments on swarms of unmanned ground vehicles under different attacks in comparison with state-of-the-art residual-based detection schemes.

This paper presents a novel method for transferring motion planning and control policies between a teacher and a learner robot. With this work, we propose to reduce the sim-to-real gap, transfer knowledge designed for a specific system into a different robot, and compensate for system aging and failures. To solve this problem we introduce a Schwarz–Christoffel mapping-based method to geometrically stretch and fit the control inputs from the teacher into the learner command space. We also propose a method based on primitive motion generation to create motion plans and control inputs compatible with the learner’s capabilities. Our approach is validated with simulations and experiments with different robotic systems navigating occluding environments.

In high-speed optical wireless communication (OWC) systems, timing errors can blur the edges of the received symbols, defined by the deviation of a signal’s timing event from its intended occurrence. The timing errors introduce the inter-symbol interference (ISI) to the neighboring symbols. In order to mitigate the degradation, a maximum likelihood (ML) based Timing Error Viterbi Equalization (TEVE) algorithm is proposed, in which the probability density functions (PDF) of the channel conditions and the timing errors are priori information. The mathematical form of each branch metric (BM) is deduced by those statistics in different situations of neighboring signs of timing errors. The summations of BMs form the cumulative metrics (CM). By adopting the add-compare-select iterations, we are able to detect symbols with the largest CMs. The theoretical closed-form average bit error rates (BER) are also deduced and compared in the cases whether the TEVE algorithm is utilized. Experimental results indicate that our proposed method can reduce timing errors’ impairment significantly with an acceptable complexity.

For safe and efficient human-robot interaction, a robot needs to predict and understand the intentions of humans who share the same space. Mobile robots are traditionally built to be reactive, moving in unnatural ways without following social protocol, hence forcing people to behave very differently from human-human interaction rules, which can be overcome if robots instead were proactive. In this paper, we build an intention-aware proactive motion planning strategy for mobile robots that coexist with multiple humans. We propose a framework that uses Hidden Markov Model (HMM) theory with a history of observations to: i) predict future states and estimate the likelihood that humans will cross the path of a robot, and ii) concurrently learn, update, and improve the predictive model with new observations at run-time. Stochastic reachability analysis is proposed to identify multiple possibilities of future states and a control scheme that leverages temporal virtual physics inspired by spring-mass systems is proposed to enable safe proactive motion planning. The proposed approach is validated with simulations and experiments involving an unmanned ground vehicle (UGV) performing go-to-goal operations in the presence of multiple humans, demonstrating improved performance and effectiveness of online learning when compared to reactive obstacle avoidance approaches.