<ul><li>A data-driven framework called Dynamics-Guided Diffusion Model (DGDM) has been developed for generating task-specific manipulator designs without task-specific training.</li><li>DGDM generates sensor-less manipulator designs that can blindly manipulate objects towards desired motions and poses using an open-loop parallel motion.</li><li>The framework represents manipulation tasks as interaction profiles and the design space using a geometric diffusion model.</li><li>DGDM outperforms optimization-based and unguided diffusion baselines in terms of the average success rate.</li></ul>

Dynamics-Guided Diffusion Model for Sensor-less Robot Manipulator Design

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