OmniGuide: Universal Spatial Guidance Fields for Enhancing Generalist Robot Policies
Overview
This research presents OmniGuide, a flexible framework designed to improve Vision-language-action (VLA) model performance on complex tasks requiring spatial understanding or precise manipulation. By expressing various guidance sources as differentiable energy functions with task-specific attractors and repellers in 3D space, the system integrates complementary strengths from sources like 3D foundation models and semantic-reasoning VLMs. Extensive experiments across simulation and real-world environments demonstrate that OmniGuide significantly enhances the success and safety rates of state-of-the-art generalist policies, including π0.5 and GR00T N1.6, matching or surpassing prior methods that incorporate specific guidance sources.
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- arXiv Robotics — research abstractsSignalOmniGuide: Universal Spatial Guidance Fields for Enhancing Generalist Robot Policies
This research presents OmniGuide, a flexible framework designed to improve Vision-language-action (VLA) model performance on complex tasks requiring spatial understanding or precise manipulation. By expressing various guidance sources as differentiable energy functions with task-specific attractors and repellers in 3D space, the system integrates complementary strengths from sources like 3D foundation models and semantic-reasoning VLMs. Extensive experiments across simulation and real-world environments demonstrate that OmniGuide significantly enhances the success and safety rates of state-of-the-art generalist policies, including π0.5 and GR00T N1.6, matching or surpassing prior methods that incorporate specific guidance sources.
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