3 papers
cs.RO2026
WoVR: World Models as Reliable Simulators for Post-Training VLA Policies with RL
Zhennan Jiang, Shangqing Zhou, Yutong Jiang +11
Reinforcement learning (RL) promises to unlock capabilities beyond imitation learning for Vision--Language--Action (VLA) models, but its requirement for massive real-world interact…
cs.RO2026
RLinf-USER: A Unified and Extensible System for Real-World Online Policy Learning in Embodied AI
Hongzhi Zang, Shu'ang Yu, Hao Lin +14
Online policy learning directly in the physical world is a promising yet challenging direction for embodied intelligence. Unlike simulation, real-world systems cannot be arbitraril…
cs.LG2025
Denoising-based Contractive Imitation Learning
Macheng Shen, Jishen Peng, Zefang Huang
A fundamental challenge in imitation learning is the \emph{covariate shift} problem. Existing methods to mitigate covariate shift often require additional expert interactions, acce…