A Survey on Reinforcement Learning Methods in Character Animation

7 March 2022

Papers citing "A Survey on Reinforcement Learning Methods in Character Animation"

7 / 7 papers shown

Title
MoConVQ: Unified Physics-Based Motion Control via Scalable Discrete Representations Heyuan Yao Zhenhua Song Yuyang Zhou Tenglong Ao Baoquan Chen Libin Liu 13 38 0 16 Oct 2023
AdaptNet: Policy Adaptation for Physics-Based Character Control Pei Xu Kaixiang Xie Sheldon Andrews P. Kry Michael Neff Morgan McGuire Ioannis Karamouzas Victor Zordan TTA 37 16 0 30 Sep 2023
Hierarchical Planning and Control for Box Loco-Manipulation Zhaoming Xie Jo-Han Tseng Sebastian Starke M. van de Panne C. Karen Liu 22 29 0 15 Jun 2023
Generating Upper-Body Motion for Real-Time Characters Making their Way through Dynamic Environments Eduardo Alvarado D. Rohmer Marie-Paule Cani AI4CE 11 8 0 21 Sep 2022
Discovering Diverse Athletic Jumping Strategies Zhiqi Yin Zeshi Yang M. van de Panne KangKang Yin 32 46 0 02 May 2021
ALLSTEPS: Curriculum-driven Learning of Stepping Stone Skills Zhaoming Xie Hung Yu Ling N. Kim M. van de Panne 112 105 0 09 May 2020
Learning to Run challenge: Synthesizing physiologically accurate motion using deep reinforcement learning L. Kidzinski Sharada Mohanty Carmichael F. Ong Jennifer Hicks Sean F. Carroll Sergey Levine M. Salathé Scott L. Delp 19 60 0 31 Mar 2018