DiffDef: A Diffusion Model for Generating Multimodal Goal Shapes From Demonstrations for Deformable Object Manipulation

Bao Thach; Tanner Watts; Siyeon Kim; Britton Jordan; Mohanraj Shanthi; Shing-Hei Ho; James M. Ferguson; Tucker Hermans; Alan Kuntz

arXiv · 2506.18779

DiffDef: A Diffusion Model for Generating Multimodal Goal Shapes From Demonstrations for Deformable Object Manipulation

Bao Thach·Tanner Watts·Siyeon Kim·Britton Jordan·Mohanraj Shanthi·Shing-Hei Ho·James M. Ferguson·Tucker Hermans·Alan Kuntz

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Abstract

Deformable object manipulation is a key capability in many robotic applications. A promising paradigm for this problem is shape servoing, which aims to control deformable objects toward desired goal shapes. However, existing approaches typically rely on impractical goal-shape acquisition methods, such as domain-knowledge engineering or manual manipulation. Moreover, prior methods generally assume a single deterministic goal and fail to handle multimodal goal settings, a common scenario in many real-world tasks where multiple distinct goal shapes can all lead to successful task completion. In this paper, we introduce DiffDef, a novel neural network that uses a diffusion model to learn a distribution of feasible goal shapes rather than predicting a single deterministic outcome. This allows DiffDef to generate diverse goal configurations while avoiding the mode-averaging artifacts common in deterministic predictors. We evaluate our method on several deformable manipulation tasks inspired by manufacturing and surgical applications, both in simulation and on two physical robotic platforms: the da Vinci Research Kit (dVRK) and a bimanual KUKA-based robotic system. The results demonstrate that DiffDef effectively captures multimodal goal distributions and significantly improves task performance in practical robotic settings. Website: sites.google.com/view/diffdef.

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Bao Thach, Tanner Watts, Siyeon Kim, Britton Jordan, Mohanraj Shanthi, Shing-Hei Ho, James M. Ferguson, Tucker Hermans, Alan Kuntz. 2025-06-23. DiffDef: A Diffusion Model for Generating Multimodal Goal Shapes From Demonstrations for Deformable Object Manipulation. https://arxiv.org/abs/2506.18779

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