Publikation
Towards Fatigue Modeling for Locomotion Tasks
Rui Xu; Noshaba Cheema; Erik Herrmann; Perttu Hämäläinen; Philipp Slusallek
In: Motion, Interaction and Games - Posters. ACM SIGGRAPH Conference on Motion in Games (MIG-2021), ACM, 2021.
Zusammenfassung
Modern deep reinforcement learning (RL) methods allow simulated characters to learn complex skills such as locomotion from scratch. To generate realistic and smooth movements, domain-specific knowledge, such as motion capture data, finite state machines or morphology-specific attributes are needed to guide the motion generation algorithms. Here we investigate biomechanical fatigue to improve symmetry and periodicity of the generated locomotion movements compared to methods found in previous literature.
Projekte
- REACT - Autonomes Fahren: Modellierungs-, Lern- und Simulationsumgebung für das Fußgängerverhalten in kritischen Verkehrssituationen
- Carousel+ - Online Tanzen mit Digitalen Charakteren