5 citations · 13 across the 9 of their papers we have counts for
4 papers · 1 filter
PARC: Physics-based Augmentation with Reinforcement Learning for Character Controllers
Michael Xu, Yi Shi, KangKang Yin +1
Humans excel in navigating diverse, complex environments with agile motor skills, exemplified by parkour practitioners performing dynamic maneuvers, such as climbing up walls and j…
Learning to Use Chopsticks in Diverse Gripping Styles
Zeshi Yang, KangKang Yin, Libin Liu
Learning dexterous manipulation skills is a long-standing challenge in computer graphics and robotics, especially when the task involves complex and delicate interactions between t…
Learning and Exploring Motor Skills with Spacetime Bounds
Li-Ke Ma, Zeshi Yang, Xin Tong +2
Equipping characters with diverse motor skills is the current bottleneck of physics-based character animation. We propose a Deep Reinforcement Learning (DRL) framework that enables…
Towards Robust Direction Invariance in Character Animation
Li-Ke Ma, Zeshi Yang, Baining Guo +1
In character animation, direction invariance is a desirable property. That is, a pose facing north and the same pose facing south are considered the same; a character that can walk…