Simulating Mid-Air Interaction Trajectories via Model Predictive Control

Titelangaben

Klar, Markus ; Fischer, Florian ; Fleig, Arthur ; Bachinski, Miroslav ; Müller, Jörg:
Simulating Mid-Air Interaction Trajectories via Model Predictive Control.
In: Baumann, Michael Heinrich ; Grüne, Lars ; Jacob, Birgit ; Worthmann, Karl (Hrsg.): Extended Abstracts presented at the 25th International Symposium on Mathematical Theory of Networks and Systems MTNS 2022 : held 12-16 September 2022 in Bayreuth, Germany. - Bayreuth , 2022 . - S. 667-670
DOI: https://doi.org/10.15495/EPub_UBT_00006809

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Abstract

We investigate the ability of Model Predictive Control (MPC) to generate humanlike movements during interaction with mid-air user interfaces, i.e., pointing in virtual reality, using a state-of-the-art biomechanical model. This model-based approach enables the analysis of interaction techniques, e.g., in terms of ergonomics and effort, without the need for extensive user studies. The model is partly a black box implemented in the MuJoCo physics engine, requiring either gradient-free optimization algorithms or a computationally expensive gradient approximation. This makes it even more important to choose optimization parameters such as the objective function or the MPC horizon length wisely. In this work, we compare various objective functions in terms of how well the simulated trajectories match real ones obtained from motion capturing. For the most promising objective function, we then analyze the effects of the horizon length and of the cost weights.