# DeformGen: Dynamics-Based Topology Augmentation for Deformable Manipulation Policy Learning Source: [DeformGen: Dynamics-Based Topology Augmentation for Deformable Manipulation Policy Learning]() Collection index: [llms.txt]() Data updated: 2026-06-30T05:00:05.200Z Authors: Zili Lin, Wenyao Zhang, Yuyang Zhang, Zekun Qi, Junyan Lin, Hanxin Zhu, Jiaolong Yang, Zhibo Chen, Yao Mu, Xiaokang Yang, Xin Jin, Wenjun Zeng Published: 2026-06-24T15:15:38.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Cloth folding on Cloth Unseen States (test)](): Cloth Folding; Cloth Unseen States (test); Success Rate: 27.5. - [Rope routing on Rope Unseen States (test)](): Rope routing; Rope Unseen States (test); Success Rate: 99. - [Toy packing on Toy Unseen States (test)](): Toy packing; Toy Unseen States (test); Success Rate: 75.5. ## Original sources and implementation [Original paper]() [PDF]()