A vision-language model fine-tuned on 572K synthetic simulation examples improves open-world mobile manipulation action decisions and object grounding over GPT-4o, with 21.9% full-task success in simulation and 90% action-generation accuracy in a small real-world test.
Mitigating hallucinations in large vision-language models by adaptively constraining information flow
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OWMM-Agent: Open World Mobile Manipulation With Multi-modal Agentic Data Synthesis
A vision-language model fine-tuned on 572K synthetic simulation examples improves open-world mobile manipulation action decisions and object grounding over GPT-4o, with 21.9% full-task success in simulation and 90% action-generation accuracy in a small real-world test.