Diffusion models have shown great potential in generating trajectory plans for agents in environments with unknown dynamics. However, such models provide no safety guarantees. In this work, we focus on risk-aware planning with respect to safety constraints and introduce a probabilistically risk-aware variant of Diffuser (PRA-Diffuser). The diffusion model initially learns a distribution over trajectories that may or may not be unsafe. We then fine-tune this model to reduce the probability of sampling such unsafe trajectories. We analyze the proposed solution and introduce a provable lower bound on risk of safety violation leveraging concentration inequalities for conditional Value-at-Risk. Our approach can be applied to models that have been pre-trained, potentially from datasets containing unsafe trajectories. Our empirical results demonstrate that our approach significantly reduces unsafe trajectories generated by the diffusion model across multiple environments.

We study multi-agent reinforcement learning (MARL) under temporally extended Signal Temporal Logic(STL) objectives, which require reasoning over both long-horizon dynamics and inter-agent relations. Wepropose TD-MAT, a transformer-based architecture with multivariate positional encodings, causal temporalmasking, and a decomposed reward based on arithmetic–geometric mean robustness with variance regularization. Experiments on coordination tasks ranging from unstructured multi-objective problems to strict temporalsequencing show that TD-MAT learns effective long-term behaviors and generalizes to heterogeneous agentsettings. Ablation studies highlight the necessity of temporal masking, positional encodings, and reward decomposition, while comparisons to MAPPO, RMAPPO, and MAT reveal that transformers provide the greatestbenefit on unstructured, long-horizon tasks.

Future generations of mobile networks are expected to contain more and more antennas with growing complexity and more parameters. Optimizing these parameters is necessary for ensuring the good performance of the network. The scale of mobile networks makes it challenging to optimize antenna parameters using manual intervention or hand-engineered strategies. Reinforcement learning is a promising technique to address this challenge but existing methods often use local optimizations to scale to large network deployments. We propose a new multi-agent reinforcement learning algorithm to optimize mobile network configurations globally. By using a value decomposition approach, our algorithm can be trained from a global reward function instead of relying on an ad-hoc decomposition of the network performance across the different cells. The algorithm uses a graph neural network architecture which generalizes to different network topologies and learns coordination behaviors. We empirically demonstrate the performance of the algorithm on an antenna tilt tuning problem and a joint tilt and power control problem in a simulated environment.

In 6G communication, many state-of-the-art ma-chine learning algorithms are going to be implemented to enhance the performances, including the latency property. In this paper, we apply Buffer Status Report (BSR) prediction to the uplink scheduling process, in order to improve the resource allocation solution currently used at the base station. According to the current solution, the base station allocates the resources based on the BSRs. However, since the BSRs do not include information for data arriving after their transmissions, the base station allocates the resources without taking into consideration the new arrival data, which may lead to the increased latency. To solve this problem, we decide to make BSR predictions at the base station side and allocate more resources than BSRs indicate. Making an accurate BSR prediction is a challenging task since there are numerous features that may influence the BSRs. Another challenge in this task is that the time intervals are tremendously short (in the order of milliseconds). After cleaning the data collected from real networks, we convert the time series forecasting problem into a supervised learning problem. State-of-the-art algorithms such as Random Forest (RF), XGboost, and Long Short Term Memory (LSTM) are leveraged to predict the data arrival rate, and one K-Fold Cross-Validation is followed to validate the models. The results show that even the time intervals are small, the data arrival rate can be predicted and the downlink data, downlink quality indicator and rank indicator can boost the forecasting performance.

This article addresses the tracking control problem of 3-D trajectories for underactuated underwater robotic vehicles operating in a constrained workspace including obstacles. More specifically, a robust nonlinear model predictive control (NMPC) scheme is presented for the case of underactuated autonomous underwater vehicles (AUVs) (i.e., unicycle-like vehicles actuated only in the surge, heave, and yaw). The purpose of the controller is to steer the unicycle-like AUV to the desired trajectory with guaranteed input and state constraints (e.g., obstacles, predefined vehicle velocity bounds, and thruster saturations) inside a partially known and dynamic environment where the knowledge of the operating workspace is constantly updated via the vehicle’s onboard sensors. In particular, considering the sensing range of the vehicle, obstacle avoidance with any of the detected obstacles is guaranteed by the online generation of a collision-free trajectory tracking path, despite the model dynamic uncertainties and the presence of external disturbances representing ocean currents and waves. Finally, realistic simulation studies verify the performance and efficiency of the proposed framework.Note to Practitioners—This article was motivated by the problem of robust trajectory tracking for an autonomous underwater vehicle (AUV) operating in an uncertain environment where the knowledge of the operating workspace (e.g., obstacle positions) is constantly updated online via the vehicle’s onboard sensors (e.g., multibeam imaging sonars and laser-based vision systems). In addition, there may be other system limitations (e.g., thruster saturation limits) and other operational constraints, induced by the need of various common underwater tasks (e.g., a predefined vehicle speed limit for inspecting the seabed, and mosaicking), where it should also be considered into the control strategy. However, based on the existing trajectory tracking control approaches for underwater robotics, there is a lack of an autonomous control scheme that provides a complete and credible control strategy that takes the aforementioned issues into consideration. Based on this, we present a reliable control strategy that takes into account the aforementioned issues, along with dynamic uncertainties of the model and the presence of ocean currents. In future research, we will extend the proposed methodology for multiple AUV performing collaborative inspection tasks in an uncertain environment.

In this paper a novel aerial manipulation system is proposed. The mechanical structure of the system, the number of thrusters and their geometry will be derived from technical optimization problems. The aforementioned problems are defined by taking into consideration the desired actuation forces and torques applied to the end-effector of the system. The framework of the proposed system is designed in a CAD Package in order to evaluate the system parameter values. Following this, the kinematic and dynamic models are developed and an adaptive backstepping controller is designed aiming to control the exact position and orientation of the end-effector in the Cartesian space. Finally, the performance of the system is demonstrated through a simulation study, where a manipulation task scenario is investigated.