# Generalization Capability

R1-V
R1 V
R1-V is a project focused on enhancing the generalization capabilities of visual language models (VLMs). Using verified reward reinforcement learning (RLVR) technology, it significantly improves the generalization abilities of VLMs in visual counting tasks, particularly excelling in out-of-distribution (OOD) tests. The significance of this technology lies in its ability to efficiently optimize large-scale models at an extremely low cost (training costs as low as $2.62), offering new insights into the practical applications of visual language models. The project is based on improvements to existing VLM training methods, aiming to enhance model performance in complex visual tasks through innovative training strategies. Its open-source nature also makes it a vital resource for researchers and developers exploring and applying advanced VLM technologies.
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SAMURAI
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ManiWAV
Maniwav
ManiWAV is a research project aimed at learning robot manipulation skills from wild audio and visual data. It collects synchronized audio and visual feedback from human demonstrations and directly learns robot manipulation policies from these demonstrations through corresponding policy interfaces. The model demonstrates its system's capabilities through four contact-rich manipulation tasks, which require the robot to passively perceive contact events and patterns or actively perceive the material and state of object surfaces. Additionally, the system can generalize to unseen wild environments by learning from diverse wild human demonstrations.
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