Vision-language-action (VLA) policies can handle noncritical stages of long-horizon manipulation yet fail during short precision-critical phases. Lightweight reinforcement learning (RL) specialists can address these failures without directly applying RL to the full VLA, but retain auxiliary controllers and switching me...
Zhen-Nan Jiang, Ting-Ying Xie, Shi-Long Ji et al.· 0 citations
PDRVLA is introduced, whose lightweight Physical Dynamics Module extracts physical tokens from vision--language features to condition action generation without online video generation, andlations show improved future prediction with world-model supervision and evaluate how subtask conditioning, future object-pose super...
Han-Qing Wang, Zhao-Tian Cai, Ruichuan An et al.· 0 citations
The proposed exploration-augmented key-observation gating first determines whether the current RL episode is judgment-worthy based on its similarity to a generated success trajectory, and proposes VLM success reward via Exploration-Augmented Key-Observation Gating (VLMeg) to well harness VLM for embodied reward design.
Xin Liu, Yu-Tong Jiang, Jia-Yue Su et al.· 0 citations
GIFT (Guided Intermediate Feature Training), an architecture-flexible framework for learning intermediate features that translates these structures into training-time constraints through geometry alignment, affordance prediction, and goal-region reconstruction, establishes learning functionally structured intermediate...
Yu-Peng Zheng, Xiang Li, Song-En Gu et al.· 2 citations
WALA, a framework for learning executable latent actions from both action-labeled demonstrations and action-free videos, achieves strong performance on RoboTwin, sets a new state-of-the-art result on RoboCasa, and improves both policy performance and generalization in real-world manipulation tasks.
