Researchers present AntiGrounding, a visual action-selection framework for robot manipulation using a dual geometric-visual trajectory interface. The framework uses a VLM to evaluate trajectory feasibility and safety, and achieves 71.25% success rate in real-world manipulation tasks. This development is relevant to AI agents as it explores multimodal reasoning and executable trajectories, with potential applications in robotics and AI-powered manipulation tasks.