Conventional robot programming methods are complex and time-consuming for users. In recent years, alternative approaches such as mixed reality have been explored to address these challenges and optimize robot programming. While the findings of the mixed reality robot programming methods are convincing, most existing methods rely on gesture interaction for robot programming. Since controller-based interactions have proven to be more reliable, this paper examines three controller-based programming methods within a mixed reality scenario: 1) Classical Jogging, where the user positions the robot's end effector using the controller's thumbsticks, 2) Direct Control, where the controller's position and orientation directly corresponds to the end effector's, and 3) Gripper Control, where the controller is enhanced with a 3D-printed gripper attachment to grasp and release objects. A within-subjects study$(n = 30)$was conducted to compare these methods. The findings indicate that the Gripper Control condition outperforms the others in terms of task completion time, user experience, mental demand, and task performance, while also being the preferred method. Therefore, it demonstrates promising potential as an effective and efficient approach for future robot programming. Video available at https://youtu.be/83kWr8zUFIQ.

Extensive training is crucial but challenging in certain areas such as explosive ordnance disposal. Past conflicts have shown that not only military personal but also civilians have to learn how to disarm unexploded ordnance. The preparation for dangerous situations is difficult and limited in the real world. Extended reality (XR) offers new possibilities to enhance the training of explosive ordnance disposal experts due to its immersive capabilities. This paper presents a comparative study (n = 75) of three distinct training methods: 1) Real-world (Real)-Training with a tangible replica object, 2) Virtual Reality (VR)-Training with a non-see-through Head-Mounted-Display (HMD), and 3) Mixed Reality (MR)-Training in a Cave Automatic Virtual Environment (CAVE). All training methods are collaborative, i.e., an instructor teaches the training content to a participant in the real or virtual world. We evaluate the suitability of these approaches in terms of usability, cognitive workload, training motivation, and training success. Our results indicate that the virtual methods, VR-Training and MR-Training, provide significantly superior results in the evaluated aspects compared to the real-world training. These results can also be applied to other collaborative training methods, as the training concept of this use case was non-specific. Therefore, these virtual technologies can increase the safety of explosive ordnance disposal personnel, and we recommend establishing this in future training.

In contrast to the real world, users are not able to perceive bystanders in virtual reality (VR). Bystanders may distract users and influence their cognitive load. This involves users to feel discomfort at the thought of unintentionally touching or even bumping into a physical bystander while interacting with the virtual environment. Not knowing the intentions of a bystander or whether one is present can unsettle the user. We investigate how a bystander affects a user’s cognitive load since it has a decisive impact on applications such as VR training. In a between-subjects lab study (N = 42), three conditions were compared: 1) no bystander, 2) an invisible bystander, and 3) a visible bystander (as an avatar). Over a series of iterations, the participants were asked to memorize four pairs of letters, perform a mental rotation task and then recall the pairs of letters. The results of our study demonstrate that a bystander acting as an avatar in the virtual environment increases the user’s cognitive load more than an invisible bystander. Moreover, the cognitive load of a VR user is significantly increased by a bystander. Therefore, our work suggests that either the examiner must be separated from the participant or the examiner’s influence (as a bystander) must be included in the analysis.