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pro vyhledávání: '"Tao, Tianxin"'
Getting up from an arbitrary fallen state is a basic human skill. Existing methods for learning this skill often generate highly dynamic and erratic get-up motions, which do not resemble human get-up strategies, or are based on tracking recorded huma
Externí odkaz:
http://arxiv.org/abs/2205.00307
Vision Transformers (ViT) have recently demonstrated the significant potential of transformer architectures for computer vision. To what extent can image-based deep reinforcement learning also benefit from ViT architectures, as compared to standard c
Externí odkaz:
http://arxiv.org/abs/2204.04905
Motion style transfer is a common method for enriching character animation. Motion style transfer algorithms are often designed for offline settings where motions are processed in segments. However, for online animation applications, such as realtime
Externí odkaz:
http://arxiv.org/abs/2203.02574
Learning to locomote is one of the most common tasks in physics-based animation and deep reinforcement learning (RL). A learned policy is the product of the problem to be solved, as embodied by the RL environment, and the RL algorithm. While enormous
Externí odkaz:
http://arxiv.org/abs/2010.04304
Autor:
Tao, Tianxin
Getting up from an arbitrary fallen state is a basic human skill. Existing methods for learning this skill often generate highly dynamic and erratic get-up motions, which do not resemble human get-up strategies, or are based on tracking recorded huma
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c0f24cbb6955958208afc4f3e712282f
Publikováno v:
IEEE ... International Conference on Rehabilitation Robotics : [proceedings] [IEEE Int Conf Rehabil Robot] 2019 Jun; Vol. 2019, pp. 95-100.
