57 citations · 64 across the 8 of their papers we have counts for
8 papers
Physics-based Scene Layout Generation from Human Motion
Jianan Li, Tao Huang, Qingxu Zhu +1
Creating scenes for captured motions that achieve realistic human-scene interaction is crucial for 3D animation in movies or video games. As character motion is often captured in a…
An Efficient Model-Based Approach on Learning Agile Motor Skills without Reinforcement
Haojie Shi, Tingguang Li, Qingxu Zhu +3
Learning-based methods have improved locomotion skills of quadruped robots through deep reinforcement learning. However, the sim-to-real gap and low sample efficiency still limit t…
Learning Highly Dynamic Behaviors for Quadrupedal Robots
Chong Zhang, Jiapeng Sheng, Tingguang Li +6
Learning highly dynamic behaviors for robots has been a longstanding challenge. Traditional approaches have demonstrated robust locomotion, but the exhibited behaviors lack diversi…
Terrain-Aware Quadrupedal Locomotion via Reinforcement Learning
Haojie Shi, Qingxu Zhu, Lei Han +3
In nature, legged animals have developed the ability to adapt to challenging terrains through perception, allowing them to plan safe body and foot trajectories in advance, which le…
Learning Terrain-Adaptive Locomotion with Agile Behaviors by Imitating Animals
Tingguang Li, Yizheng Zhang, Chong Zhang +5
In this paper, we present a general learning framework for controlling a quadruped robot that can mimic the behavior of real animals and traverse challenging terrains. Our method c…
SwinGar: Spectrum-Inspired Neural Dynamic Deformation for Free-Swinging Garments
Tianxing Li, Rui Shi, Qing Zhu +1
Our work presents a novel spectrum-inspired learning-based approach for generating clothing deformations with dynamic effects and personalized details. Existing methods in the fiel…
Lifelike Agility and Play in Quadrupedal Robots using Reinforcement Learning and Generative Pre-trained Models
Lei Han, Qingxu Zhu, Jiapeng Sheng +16
Knowledge from animals and humans inspires robotic innovations. Numerous efforts have been made to achieve agile locomotion in quadrupedal robots through classical controllers or r…
Neural Categorical Priors for Physics-Based Character Control
Qingxu Zhu, He Zhang, Mengting Lan +1
Recent advances in learning reusable motion priors have demonstrated their effectiveness in generating naturalistic behaviors. In this paper, we propose a new learning framework in…