2 citations · 2 across the 5 of their papers we have counts for
5 papers
VAMPO: Policy Optimization for Improving Visual Dynamics in Video Action Models
Zirui Ge, Pengxiang Ding, Baohua Yin +16
Video action models are an appealing foundation for Vision--Language--Action systems because they can learn visual dynamics from large-scale video data and transfer this knowledge…
VLA-RFT: Vision-Language-Action Reinforcement Fine-tuning with Verified Rewards in World Simulators
Hengtao Li, Pengxiang Ding, Runze Suo +8
Vision-Language-Action (VLA) models enable embodied decision-making but rely heavily on imitation learning, leading to compounding errors and poor robustness under distribution shi…
Efficient Online RL Fine Tuning with Offline Pre-trained Policy Only
Wei Xiao, Jiacheng Liu, Zifeng Zhuang +3
Improving the performance of pre-trained policies through online reinforcement learning (RL) is a critical yet challenging topic. Existing online RL fine-tuning methods require con…
OpenHelix: A Short Survey, Empirical Analysis, and Open-Source Dual-System VLA Model for Robotic Manipulation
Can Cui, Pengxiang Ding, Wenxuan Song +10
Dual-system VLA (Vision-Language-Action) architectures have become a hot topic in embodied intelligence research, but there is a lack of sufficient open-source work for further per…
TDMPBC: Self-Imitative Reinforcement Learning for Humanoid Robot Control
Zifeng Zhuang, Diyuan Shi, Runze Suo +5
Complex high-dimensional spaces with high Degree-of-Freedom and complicated action spaces, such as humanoid robots equipped with dexterous hands, pose significant challenges for re…