Tim Schreiter

Ansgar Howey, Tim Schreiter, Andrey Rudenko et al.

Automated Guided Vehicles (AGV) in factory automation are increasingly capable of moving autonomously in close proximity to human workers. While their physical safety is regulated by standards and directives, perceived safety and workers comfort in close-proximity interactions are being actively investigated in studies. There are three limitations in the prior art research to that end. Firstly, AGVs with larger payloads are understudied. Secondly, the test participants are usually students and not working professionals. Thirdly, while conducting in-person experiments with heavy machinery can be dangerous, the transfer of safety perception results from simulated experiments remains open. In this paper, we investigate industrial workers perceived safety in shared spaces with large AGVs in a real-world encounter and in virtual reality. We vary the passing distance and the shape of the collision avoidance maneuver, and evaluate perceived threat level using a handheld pressure-sensitive trigger interface and a post-experiment questionnaire. Additionally, we ask participants to set their own collision avoidance parameters based on their experience with the demonstrated trajectory profiles. In a within-subject study, we found that, while the threat levels are perceived overall slightly higher in VR, the passing distance of 1.5 to 2 meters is preferred among the demonstrated profiles, as well as in the self-defined trajectories.

Tiago Rodrigues de Almeida, Tim Schreiter, Andrey Rudenko et al.

Accurate human activity and trajectory prediction are crucial for ensuring safe and reliable human-robot interactions in dynamic environments, such as industrial settings, with mobile robots. Datasets with fine-grained action labels for moving people in industrial environments with mobile robots are scarce, as most existing datasets focus on social navigation in public spaces. This paper introduces the THÖR-MAGNI Act dataset, a substantial extension of the THÖR-MAGNI dataset, which captures participant movements alongside robots in diverse semantic and spatial contexts. THÖR-MAGNI Act provides 8.3 hours of manually labeled participant actions derived from egocentric videos recorded via eye-tracking glasses. These actions, aligned with the provided THÖR-MAGNI motion cues, follow a long-tailed distribution with diversified acceleration, velocity, and navigation distance profiles. We demonstrate the utility of THÖR-MAGNI Act for two tasks: action-conditioned trajectory prediction and joint action and trajectory prediction. We propose two efficient transformer-based models that outperform the baselines to address these tasks. These results underscore the potential of THÖR-MAGNI Act to develop predictive models for enhanced human-robot interaction in complex environments.

Janik Kaden, Maximilian Hilger, Tim Schreiter et al.

With robots increasingly integrating into human environments, understanding and predicting human motion is essential for safe and efficient interactions. Modern human motion and activity prediction approaches require high quality and quantity of data for training and evaluation, usually collected from motion capture systems, onboard or stationary sensors. Setting up these systems is challenging due to the intricate setup of hardware components, extensive calibration procedures, occlusions, and substantial costs. These constraints make deploying such systems in new and large environments difficult and limit their usability for in-the-wild measurements. In this paper we investigate the possibility to apply the novel Ultra-Wideband (UWB) localization technology as a scalable alternative for human motion capture in crowded and occlusion-prone environments. We include additional sensing modalities such as eye-tracking, onboard robot LiDAR and radar sensors, and record motion capture data as ground truth for evaluation and comparison. The environment imitates a museum setup, with up to four active participants navigating toward random goals in a natural way, and offers more than 130 minutes of multi-modal data. Our investigation provides a step toward scalable and accurate motion data collection beyond vision-based systems, laying a foundation for evaluating sensing modalities like UWB in larger and complex environments like warehouses, airports, or convention centers.