October 2, 2025Open Access

AntiGrounding: Lifting Robotic Actions into VLM Representation Space for Decision Making

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The AntiGrounding framework generates optimal robot trajectories using vision-language models.
Zero-shot synthesis enables performance on new tasks without prior training, showcasing adaptability.
Structured visual question answering enhances decision-making by integrating spatial and semantic details.
Experiments demonstrate superior performance in robotic manipulation tasks compared to existing baselines.

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AntiGrounding lifts robotic actions into VLM representation space for optimal trajectory synthesis, suggesting improved manipulation efficiency.

Li et al. (2025) studied this question.

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AntiGrounding lifts robotic actions into VLM representation space for optimal trajectory synthesis, suggesting improved manipulation efficiency.