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Processes multimodal inputs including natural language and camera images to generate motor commands for generalized robot manipulation.
Distinct from Vision-Language Models: Distinct from Vision-Language Models: extends vision-language processing to generate motor commands (action output), not just visual and linguistic understanding.
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Processes multimodal inputs including natural language and camera images to generate motor commands for generalized robot manipulation.
OpenVLA is a vision-language-action model and framework designed for general-purpose robotic manipulation. It provides a robotic policy training framework and a control inference engine that map visual and textual inputs to robotic control actions, enabling zero-shot instruction following on hardware. The project includes a robotics dataset pipeline for standardizing diverse trajectory data and managing dataset mixtures. It supports large-scale model training through distributed GPU compute and sharded data parallelism, alongside parameter-efficient adaptation for fine-tuning models to new ta
Maps visual and textual inputs to tokenized action sequences for general-purpose robotic manipulation.