In this paper, we propose a reinforcement learning-based end-to-end learning method for the autonomous driving of a mobile robot in a dynamic environment with obstacles. Applying two additional techniques for reinforcement learning simultaneously helps the mobile robot in finding an optimal policy to reach the destination without collisions. First, the multifunctional reward-shaping technique guides the agent toward the goal by utilizing information about the destination and obstacles. Next, employing the hindsight experience replay technique to address the experience imbalance caused by the sparse reward problem assists the agent in finding the optimal policy. We validated the proposed technique in both simulation and real-world environments. To assess the effectiveness of the proposed method, we compared experiments for five different cases.

This paper proposes a task decomposition and dedicated reward-system-based reinforcement learning algorithm for the Pick-and-Place task, which is one of the high-level tasks of robot manipulators. The proposed method decomposes the Pick-and-Place task into three subtasks: two reaching tasks and one grasping task. One of the two reaching tasks is approaching the object, and the other is reaching the place position. These two reaching tasks are carried out using each optimal policy of the agents which are trained using Soft Actor-Critic (SAC). Different from the two reaching tasks, the grasping is implemented via simple logic which is easily designable but may result in improper gripping. To assist the grasping task properly, a dedicated reward system for approaching the object is designed through using individual axis-based weights. To verify the validity of the proposed method, wecarry out various experiments in the MuJoCo physics engine with the Robosuite framework. According to the simulation results of four trials, the robot manipulator picked up and released the object in the goal position with an average success rate of 93.2%.

In this paper, we present LaMPC-CBF, a language-guided control framework that integrates model predictive control (MPC) with a control barrier function (CBF) to generate provably safe trajectories from free-form user instructions for robotic systems. Large language models translate natural language, including varying levels of user-expressed safety intent, into optimization objectives and CBF-based safety constraints. In particular, the expressed safety intent is mapped to numerical safety parameters within the CBF constraints, which directly govern the system’s obstacle avoidance behavior. This automated parameterization bridges the gap between abstract human linguistic expressions and rigorous mathematical safety guarantees, ensuring that the robot’s behavior aligns precisely with the user’s perceived safety preferences. Additionally, LaMPC-CBF supports interactive user feedback during task execution. This allows users to redefine safety intents via natural language on the fly, eliminating the need for controller recompilation. This approach allows non-experts to adjust parameters without manual tuning by experts easily and enables online adaptation to evolving user’s safety intent. Simulation results demonstrate that LaMPC-CBF improves safety and adaptability compared to the baseline, particularly in dynamic obstacle scenarios.

This article is devoted to studying the homogeneous polynomial parameter-dependent (HPPD)-type control strategy of active vehicle suspension systems (AVSSs) within a cyber-physical architecture that is subjected to malicious attacks. A framework based on cyber-physical systems is proposed for designing the controller parameters of an AVSS, considering the vulnerability of control signals in the transport layer to malicious cyberattacks. To address the hybrid attacks consisting of denial of service and false data injection (FDI), a resilient controller with FDI attack compensation is designed, and an augmented error system model under hybrid attacks is established. Subsequently, to further enhance handling stability and ride comfort, a homogeneous polynomial method is introduced, and a novel HPPD-type resilient controller with FDI attack compensation is devised. The co-design method for the HPPD-type controller and FDI attack observer is derived using a time-varying Lyapunov function to ensure the exponential stability of the HPPD-type augmented error system. Finally, the effectiveness of the proposed method in enhancing stability and improving comfort is verified through software simulations and hardware-in-the-loop experiments, and the superiority of the method in resisting attacks is demonstrated through comparative analysis of some performance indicators.