VerifLM: Physically Verified Image-Edit-Guided Goal Pose Generation for Robotic Manipulation
Overview
This paper introduces VerifLM, a framework that translates image-edited goal configurations into physically verified 6-DoF object poses for robotic manipulation. The system reconstructs metric object geometry from RGB-D observations without predefined meshes or physical parameters, then uses a vision-language model to generate axis-wise sampling constraints and a task-specific validation program. Candidate poses are evaluated in parallel-world physics simulations with varying geometry and physics, and the VLM selects the most task-consistent candidate. Evaluated on nine manipulation tasks, VerifLM achieves the highest success rate on eight of them, showing the largest performance gain for goals requiring precise orientation and contact relationships.
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- arXiv Robotics — research abstractsSignalVerifLM: Physically Verified Image-Edit-Guided Goal Pose Generation for Robotic Manipulation
This paper introduces VerifLM, a framework that translates image-edited goal configurations into physically verified 6-DoF object poses for robotic manipulation. The system reconstructs metric object geometry from RGB-D observations without predefined meshes or physical parameters, then uses a vision-language model to generate axis-wise sampling constraints and a task-specific validation program. Candidate poses are evaluated in parallel-world physics simulations with varying geometry and physics, and the VLM selects the most task-consistent candidate. Evaluated on nine manipulation tasks, VerifLM achieves the highest success rate on eight of them, showing the largest performance gain for goals requiring precise orientation and contact relationships.
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