DeformGen: Dynamics-Based Topology Augmentation for Deformable Manipulation Policy Learning

• URL: http://arxiv.org/abs/2606.25939v2
• Date: Fri, 26 Jun 2026 02:40:52 GMT
• Title: DeformGen: Dynamics-Based Topology Augmentation for Deformable Manipulation Policy Learning
• Abstract summary: We present DeformGen, a dynamics-based augmentation framework for deformable objects.<n>For the state challenge, DeformGen expands the valid state distribution by applying localized physical disturbances.<n>For the trajectory challenge, DeformGen transfers source manipulation trajectories via deformation-field warping.
• Score: 65.83886420120952
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Demonstration augmentation is proposed for cost-efficient data acquisition, but existing methods are fundamentally limited in deformable manipulation due to two challenges: (1) the state space is high-dimensional with physics-induced constraints, making valid configurations impossible to reach via low-dimensional pose perturbations; and (2) trajectory transfer is non-equivariant, as material points no longer move rigidly together under deformation. We present DeformGen, a dynamics-based augmentation framework that achieves topological diversity for deformable objects. For the state challenge, DeformGen expands the valid state distribution by applying localized physical disturbances and forward-simulating the dynamics to obtain topology-coherent, physically plausible deformable states. For the trajectory challenge, DeformGen transfers source manipulation trajectories via deformation-field warping, which lifts per-particle displacements into a continuous spatial function to adapt the end-effector trajectory consistently with the deformed geometry. In this way, our method jointly augments the state distribution and its associated manipulation behavior. Experiments on high-fidelity deformable manipulation benchmarks show that DeformGen generally improves policy learning compared with training on the original demonstrations alone and with rigid-style augmentation baselines.

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