Publication
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.
Abstract
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.
Projects
- REACT - Autonomous Driving: Modeling, learning and simulation environment for pedestrian behavior in critical traffic situations
- Carousel+ - Embodied Online Dancing with Digital Characters