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

Understand

We present Dynamics-Guided Diffusion Model (DGDM), a data-driven framework for generating task-specific manipulator designs without task-specific training.

• Given object shapes and task specifications, DGDM generates sensor-less manipulator designs that can blindly manipulate objects towards desired motions and poses using an open-loop parallel motion.
• This framework 1) flexibly represents manipulation tasks as interaction profiles, 2) represents the design space using a geometric diffusion model, and 3) efficiently searches this design space using the gradients provided by a dynamics network trained without any task information.
• We evaluate DGDM on various manipulation tasks ranging from shifting/rotating objects to converging objects to a specific pose.