VLA-Adapter is a micro vision-language-action model trained on Libero-Spatial. It adopts the Prismatic-VLM architecture and only uses Qwen2.5-0.5B as the large language model backbone. This model outperforms open-source VLA models with larger parameter scales in robot benchmarks, achieving high-performance vision-language-action understanding and execution.

MultimodalSafetensorsEnglish

Hume-System2 is the pre-trained weights of System 2 for a dual-system Vision-Language-Action (VLA) model, used to accelerate the training of System 2 and provide support for relevant research and applications in the field of robotics.

MultimodalTransformersEnglish

VLAA-Thinker is an innovative vision-language model that can process both image and text inputs simultaneously and generate high-quality text outputs. This model is developed based on the research results of the paper 'SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language Models' and focuses on R1-like reasoning abilities.

MultimodalTransformers

VLAA-Thinker-Qwen2.5-3B is a large visual language model for reasoning similar to R1, focusing on multimodal reasoning tasks. This model has achieved state-of-the-art performance on the OpenCompass multimodal reasoning leaderboard and supports image understanding and complex reasoning abilities.

MultimodalTransformers

CogACT is a novel advanced Vision-Language-Action (VLA) architecture derived from Vision-Language Models (VLM), specifically designed for robot manipulation.

MultimodalTransformersEnglish

CogACT is a novel advanced Vision-Language-Action (VLA) architecture derived from Vision-Language Models (VLM), specifically designed for robot manipulation.

MultimodalTransformersEnglish

CogACT is a novel Vision-Language-Action (VLA) architecture that combines vision-language models with specialized action modules for robotic manipulation tasks.

MultimodalTransformersEnglish