This study explores the intricate dynamics of trust in human–robot interaction (HRI), particularly in the context of modern robotic systems enhanced by artificial intelligence (AI). By grounding our investigation in the principles of interpersonal trust, we identify and analyze both similarities and differences between trust in human–human interactions and human–robot scenarios. A key aspect of our research is the clear definition and characterization of trust in HRI, including the identification of factors influencing its development. Our empirical findings reveal that trust in HRI is not static but varies dynamically with the complexity of the tasks involved. Notably, we observe a stronger tendency to trust robots in tasks that are either very straightforward or highly complex. In contrast, for tasks of intermediate complexity, there is a noticeable decline in trust. This pattern of trust challenges conventional perceptions and emphasizes the need for nuanced understanding and design in HRI. Our study provides new insights into the nature of trust in HRI, highlighting its dynamic nature and the influence of task complexity, thereby offering a valuable reference for future research in the field.

Collaborative autonomous landing of a quadrotor Unmanned Aerial Vehicle (UAV) on a moving Unmanned Ground Vehicle (UGV) presents challenges due to the need for accurate real-time tracking of the UGV and the adjustment for the landing policy. To address this challenge, we propose a progressive learning framework for generating an optimal landing policy based on vision without the need of communication between the UAV and the UGV. First, we propose the Landing Vision System (LVS) to offer rapid localization and pose estimation of the UGV. Then, we design an Automatic Curriculum Learning (ACL) approach to learn the landing tasks under different conditions of UGV motions and wind interference. Specifically, we introduce a neural network-based difficulty discriminator to schedule the landing tasks according to their levels of difficulty. Our method achieves a higher landing success rate and accuracy compared with the state-of-the-art TD3 reinforcement learning algorithm.

Path planning using handcrafted waypoints is inefficient for a multi-task UAV operating in dynamic environments with potential risks such as bad weather, obstacles, or forbidden zones, among others. In this paper, we propose an automatic path planning method through natural language that instructs the UAV with compound commands about the tasks and the corresponding regions in a given map. First, we analyze the characteristics of the tasks and we model each task with a parameterized zone. Then, we use deep neural networks to segment the natural language commands into a sequence of labeled words, from which the semantics are extracted to select the waypoints and trajectory patterns accordingly. Finally, paths between the waypoints are generated using rapidly exploring random trees (RRT) or Dubins curves based on the task requirements. We demonstrate the effectiveness of the proposed method using a simulated quadrotor UAV that follows sequential commands in four typical tasks with potential risks.