arXiv subjects

Malte Teichmann

Publications and source records attributed to Malte Teichmann.

2 recordsLinked to original sources

arXiv2026

Robot Programming with Augmented Reality: The Role of Spatial Ability

Programming a robot arm requires users to interpret coordinate frames, joint rotations, and trajectories that are not directly visible. Augmented reality (AR) can make these spatial relations visible, but its benefits may depend on users' spatial ability. We conducted a randomized between-subjects experiment ($N=71$) in which participants learned to program a physical UR5e robot using either conventional teach-pendant controls with PDF instructions or a head-mounted AR interface that displayed joints, coordinate frames, and waypoints. We measured users' spatial ability with the Mental Rotation Test and assessed subjective cognitive load and system usability. Overall, AR did not significantly improve cognitive load or usability compared with conventional instruction. However, exploratory analyses revealed a compensatory effect: spatial ability predicted higher usability and lower extraneous cognitive load in the control group, but not in the AR condition, suggesting AR mitigated the disadvantage typically faced by users with lower spatial ability. These findings point to a compensatory function of AR that should be explored to guide the design of personalized AR interfaces for human-robot interaction.

cs.RO↗

arXiv2026

Beyond Static Instruction: A Multi-agent AI Framework for Adaptive Augmented Reality Robot Training

Augmented Reality (AR) offers powerful visualization capabilities for industrial robot training, yet current interfaces remain predominantly static, failing to account for learners' diverse cognitive profiles. In this paper, we present an AR application for robot training and propose a multi-agent AI framework for future integration that bridges the gap between static visualization and pedagogical intelligence. We report on the evaluation of the baseline AR interface with 36 participants performing a robotic pick-and-place task. While overall usability was high, notable disparities in task duration and learner characteristics highlighted the necessity for dynamic adaptation. To address this, we propose a multi-agent framework that orchestrates multiple components to perform complex preprocessing of multimodal inputs (e.g., voice, physiology, robot data) and adapt the AR application to the learner's needs. By utilizing autonomous Large Language Model (LLM) agents, the proposed system would dynamically adapt the learning environment based on advanced LLM reasoning in real-time.

cs.RO↗