In the field of the Internet of Unmanned Agents (IUA), autonomous devices often struggle to maintain stability, adaptability, and coordination in dynamic environments. Current control strategies are hindered by difficulties such as real-time adaptation to environmental changes, handling sensor noise, and managing the coordination of multiple agents under complex, variable task constraints. These challenges limit the practical deployment of robots in IUA systems, making it crucial to develop more robust and efficient control methods. To address these issues, we propose a reinforcement learning-based impedance coefficient optimization approach to improve robots’ stability and responsiveness. By integrating impedance control, the Divergent Component of Motion (DCM) model, reinforcement learning (RL), and quadratic programming (QP), this approach dynamically adjusts the robot’s stiffness, damping, and inertia coefficients in real-time. Specifically, the Soft Actor-Critic (SAC) algorithm is used for optimization, while QP ensures that control forces remain feasible across multiple tasks and constraints. This strategy enhances the robots’ adaptability and coordination in complex IUA. Experimental results validate the approach, significantly improving robot performance in IUA systems.

Aspect-based sentiment analysis (ABSA) aims to identify specific sentiment elements in social multimedia content. To address aspect extraction and sentiment prediction together, recent studies have utilized a sequence tagging approach, mainly leveraging pretrained language models (PLMs) with specific architecture and auxiliary subtasks. However, these approaches often overlook task-related knowledge and struggle to scale across different domains. With advances in large language models (LLMs), there is a rising trend in constructing generative ABSA models. Nevertheless, these techniques tend to emphasize specific frameworks and overlook comprehensive knowledge representation. To address these challenges while leveraging the advantages of LLM and PLM-based methods, we propose a hybrid knowledge integration framework (HFABGKI). It employs a parameter-efficient fine-tuning technique, allowing for plug-and-play integration with existing LLMs. To bridge the LLM and PLM-based models, HF-ABGKI incorporates a global label semantic representation for potential aspect tokens, in which a simplified gating mechanism is proposed to filter useful information. Experimental results from six public social multimedia datasets demonstrate that our approach can accurately extract aspect terms and predict their sentiment polarity, achieving state-of-the-art performance compared to existing ABSA methods.

Instant delivery services, such as food delivery and package delivery, have achieved explosive growth in recent years by providing customers with daily-life convenience. An emerging research area within these services is service Route&Time Prediction (RTP), which aims to estimate the future service route as well as the arrival time of a given worker. As one of the most crucial tasks in those service platforms, RTP stands central to enhancing user satisfaction and trimming operational expenditures on these platforms. Despite a plethora of algorithms developed to date, there is no systematic, comprehensive survey to guide researchers in this domain. To fill this gap, our work presents the first comprehensive survey that methodically categorizes recent advances in service route and time prediction. We start by defining the RTP challenge and then delve into the metrics that are often employed. Following that, we scrutinize the existing RTP methodologies, presenting a novel taxonomy of them. We categorize these methods based on three criteria: (i) type of task, subdivided into only-route prediction, only-time prediction, and joint route&time prediction; (ii) model architecture, which encompasses sequence-based and graph-based models; and (iii) learning paradigm, including Supervised Learning (SL) and Deep Reinforcement Learning (DRL). Conclusively, we highlight the limitations of current research and suggest prospective avenues. We believe that the taxonomy, progress, and prospects introduced in this paper can significantly promote the development of this field.