Journal Article10.1111/CGF.12571
Improving Sampling-based Motion Control
TL;DR: This work improves the sampling‐based motion control method of Liu et at by learning from the past control reconstruction trials through sample distribution adaptation and using a sliding window scheme for better performance and an averaging method for noise reduction.
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Abstract: We address several limitations of the sampling-based motion control method of Liu et at [LYvdP* 10] The key insight is to learn from the past control reconstruction trials through sample distribution adaptation Coupled with a sliding window scheme for better performance and an averaging method for noise reduction, the improved algorithm can efficiently construct open-loop controls for long and challenging reference motions in good quality Our ideas are intuitive and the implementations are simple We compare the improved algorithm with the original algorithm both qualitatively and quantitatively, and demonstrate the effectiveness of the improved algorithm with a variety of motions ranging from stylized walking and dancing to gymnastic and Martial Arts routines
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Citations
DReCon: data-driven responsive control of physics-based characters
TL;DR: This work proposes a two-step approach for building responsive simulated character controllers from unstructured motion capture data that emphasizes responsiveness to user input, visual quality, and low runtime cost for application in video-games.
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Guided Learning of Control Graphs for Physics-Based Characters
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Learning to Schedule Control Fragments for Physics-Based Characters Using Deep Q-Learning
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TL;DR: This article describes how to learn a scheduling scheme that reorders short control fragments as necessary at runtime to create a control system that can respond to disturbances and allows steering and other user interactions.
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Physics-based motion capture imitation with deep reinforcement learning
Nuttapong Chentanez,Matthias Müller,Miles Macklin,Viktor Makoviychuk,Stefan Jeschke +4 more
- 08 Nov 2018
TL;DR: A deep reinforcement learning method that learns to control articulated humanoid bodies to imitate given target motions closely when simulated in a physics simulator is introduced and it is demonstrated that the proposed method can control the character to imitate a wide variety of motions.
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Josh Merel,Arun Ahuja,Vu Pham,Saran Tunyasuvunakool,Siqi Liu,Dhruva Tirumala,Nicolas Heess,Greg Wayne +7 more
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