3 papers
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.RO2026
Speedup Patch: Learning a Plug-and-Play Policy to Accelerate Embodied Manipulation
Zhichao Wu, Junyin Ye, Zhilong Zhang +6
While current embodied policies exhibit remarkable manipulation skills, their execution remains unsatisfactorily slow as they inherit the tardy pacing of human demonstrations. Exis…
cs.RO2026
Towards Practical World Model-based Reinforcement Learning for Vision-Language-Action Models
Zhilong Zhang, Haoxiang Ren, Yihao Sun +6
Vision-Language-Action (VLA) models show strong generalization for robotic control, but finetuning them with reinforcement learning (RL) is constrained by the high cost and safety…