Keywords: manipulator design, hardware optimization, diffusion model
TL;DR: Generating task-specific manipulator designs without task-specific training
Abstract: 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. Our generated designs outperform optimization-based and unguided diffusion baselines relatively by 31.5\% and 45.3\% on average success rate. With the ability to generate a new design within 0.8s, DGDM facilitates rapid design iteration and enhances the adoption of data-driven approaches for robot mechanism design. Qualitative results are best viewed on our project website https://dgdmcorl.github.io.
Supplementary Material: zip
Spotlight Video: mp4
Video: https://www.youtube.com/watch?v=8KSW1_zaGJU
Website: https://dgdm-robot.github.io/
Code: https://github.com/real-stanford/dgdm
Publication Agreement: pdf
Student Paper: yes
Submission Number: 98
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