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Abstract This paper introduces an innovative and streamlined design of a robot, resembling a bicycle, created to effectively inspect a wide range of ferromagnetic structures, even those with intricate shapes. The key highlight of this robot lies in its mechanical simplicity coupled with remarkable agility. The locomotion strategy hinges on the arrangement of two magnetic wheels in a configuration akin to a bicycle, augmented by two independent steering actuators. This configuration grants the robot the exceptional ability to move in multiple directions. Moreover, the robot employs a reciprocating mechanism that allows it to alter its shape, thereby surmounting obstacles effortlessly. An inherent trait of the robot is its innate adaptability to uneven and intricate surfaces on steel structures, facilitated by a dynamic joint. To underscore its practicality, the robot's application is demonstrated through the utilization of an ultrasonic sensor for gauging steel thickness, coupled with a pragmatic deployment mechanism. By integrating a defect detection model based on deep learning, the robot showcases its proficiency in automatically identifying and pinpointing areas of rust on steel surfaces. The paper undertakes a thorough analysis, encompassing robot kinematics, adhesive force, potential sliding and turn‐over scenarios, and motor power requirements. These analyses collectively validate the stability and robustness of the proposed design. Notably, the theoretical calculations established in this study serve as a valuable blueprint for developing future robots tailored for climbing steel structures. To enhance its inspection capabilities, the robot is equipped with a camera that employs deep learning algorithms to detect rust visually. The paper substantiates its claims with empirical evidence, sharing results from extensive experiments and real‐world deployments on diverse steel bridges, situated in both Nevada and Georgia. These tests comprehensively affirm the robot's proficiency in adhering to surfaces, navigating challenging terrains, and executing thorough inspections. A comprehensive visual representation of the robot's trials and field deployments is presented in videos accessible at the following links:https://youtu.be/Qdh1oz_oxiQ andhttps://youtu.be/vFFq79O49dM. 

Human-robot interaction (HRI) studies have found people overtrust robots in domestic settings, even when the robot exhibits faulty behavior. Cognitive dissonance and selective attention explain these results. To test these theories, a novel HRI study was performed in a university library where participants were recruited to follow a package delivery robot. Participants then faced a dilemma to deliver a package in a private common room that might be off-limits. Then, they faced another dilemma when the robot stopped in front of an Emergency Exit door, and they had to trust the robot whether to open it or not Results showed individuals did not overtrust the robot and open the Emergency Exit door. Interestingly, most individuals demurred from entering the private common room when packages were not labeled, whereas groups of friends were more likely to enter the room. Then, selective attention was demonstrated by stopping participants in front of a similar Emergency Exit door and assessing whether they noticed it In one condition, only half of participants noticed it, and when the robot became more engaging no one noticed it. Additionally, a malfunctioning robot is exhibited, showing what kind of negative outcome was required to reduce trust.