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Guided Learning of Control Graphs for Physics-Based Characters
Ist Teil von
ACM transactions on graphics, 2016-06, Vol.35 (3), p.1-14
Erscheinungsjahr
2016
Quelle
ACM Digital Library
Beschreibungen/Notizen
The difficulty of developing control strategies has been a primary bottleneck in the adoption of physics-based simulations of human motion. We present a method for learning robust feedback strategies around given motion capture clips as well as the transition paths between clips. The output is a control graph that supports real-time physics-based simulation of multiple characters, each capable of a diverse range of robust movement skills, such as walking, running, sharp turns, cartwheels, spin-kicks, and flips. The control fragments that compose the control graph are developed using guided learning. This leverages the results of open-loop sampling-based reconstruction in order to produce state-action pairs that are then transformed into a linear feedback policy for each control fragment using linear regression. Our synthesis framework allows for the development of robust controllers with a minimal amount of prior knowledge.
Sprache
Englisch
Identifikatoren
ISSN: 0730-0301
eISSN: 1557-7368
DOI: 10.1145/2893476
Titel-ID: cdi_crossref_primary_10_1145_2893476
Format
–
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