7 papers
Learning Context-Aware Motion Priors for Humanoid Control
Yunyang Mo, Yi Gu, Yangchen Zhou +2
Motion priors provide powerful guidance for learning naturalistic humanoid behaviors. However, existing methods typically learn a general, task-agnostic prior from the entire refer…
PFM-HR: Pose Flow Matching for Humanoid Robots
Yukang Gao, Yi Gu, Yangchen Zhou +9
Motion priors improve reinforcement learning for physics-based humanoid tracking, but temporal priors require ordered motion clips, while pose priors provide limited guidance for p…
RMBench: Memory-Dependent Robotic Manipulation Benchmark with Insights into Policy Design
Tianxing Chen, Yuran Wang, Mingleyang Li +16
Robotic manipulation policies have made rapid progress in recent years, yet most existing approaches give limited consideration to memory capabilities. Consequently, they struggle…
Sparse View Distractor-Free Gaussian Splatting
Yi Gu, Zhaorui Wang, Jiahang Cao +4
3D Gaussian Splatting (3DGS) enables efficient training and fast novel view synthesis in static environments. To address challenges posed by transient objects, distractor-free 3DGS…
Spherical Latent Motion Prior for Physics-Based Simulated Humanoid Control
Jing Tan, Weisheng Xu, Xiangrui Jiang +11
Learning motion priors for physics-based humanoid control is an active research topic. Existing approaches mainly include variational autoencoders (VAE) and adversarial motion prio…
DexFormer: Cross-Embodied Dexterous Manipulation via History-Conditioned Transformer
Ke Zhang, Lixin Xu, Chengyi Song +4
Dexterous manipulation remains one of the most challenging problems in robotics, requiring coherent control of high-DoF hands and arms under complex, contact-rich dynamics. A major…