4 papers
PIGDreamer: Privileged Information Guided World Models for Safe Partially Observable Reinforcement Learning
Dongchi Huang, Jiaqi Wang, Yang Li +3
Partial observability presents a significant challenge for Safe Reinforcement Learning (Safe RL), as it impedes the identification of potential risks and rewards. Leveraging specif…
CO-RFT: Efficient Fine-Tuning of Vision-Language-Action Models through Chunked Offline Reinforcement Learning
Dongchi Huang, Zhirui Fang, Tianle Zhang +3
Vision-Language-Action (VLA) models demonstrate significant potential for developing generalized policies in real-world robotic control. This progress inspires researchers to explo…
Dexterous Grasping with Real-World Robotic Reinforcement Learning
Dongchi Huang, Tianle Zhang, Yihang Li +5
Dexterous grasping in the real world presents a fundamental and significant challenge for robot learning. The ability to employ affordance-aware poses to grasp objects with diverse…
SafeDreamer: Safe Reinforcement Learning with World Models
Weidong Huang, Jiaming Ji, Chunhe Xia +2
The deployment of Reinforcement Learning (RL) in real-world applications is constrained by its failure to satisfy safety criteria. Existing Safe Reinforcement Learning (SafeRL) met…