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cs.RO2026
VINE: Taming Generative Control Policies for Reinforcement Learning
Rushuai Yang, Zhuo Han, Houlin Li +10
Flow-matching policies have emerged as an effective policy parameterization for robot learning. They iteratively generate actions from noise, enabling highly expressive modeling of…
cs.AI2026
Bellman-Taylor Score Decoding for Markov Decision Processes with State-Dependent Feasible Action Sets
Yi Chen, Rushuai Yang, Qiang Chen +2
Many Markov decision processes (MDPs) in operations research have feasible actions that are state dependent and defined implicitly by various operational constraints. These feature…
cs.RO2026
ALOE: Action-Level Off-Policy Evaluation for Vision-Language-Action Model Post-Training
Rushuai Yang, Hecheng Wang, Zhichao Wu +11
We study how to improve large foundation vision-language-action (VLA) systems through human-in-the-loop reinforcement learning (RL) in real-world environments. A key challenge is l…