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Deformable objects

Deformable objects are physical entities whose shape, volume, or geometric configuration can change when subjected to external forces or manipulation. Unlike rigid bodies, in which the relative distances between all internal points remain fixed during motion, deformable objects undergo transformations such as bending, stretching, twisting, or compression. These objects encompass a diverse range of materials, including linear entities like ropes and cables, planar materials like cloth and paper, and volumetric or viscous items like foam, soft tissues, and dough. Because their configurations cannot be described solely by standard rigid-body poses, deformable objects are characterized by high-dimensional or infinite degrees of freedom, complex internal dynamics, and non-linear responses to contact and force.

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

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

Zili Lin, Wenyao Zhang, Yuyang Zhang, Zekun Qi, Junyan Lin, Hanxin Zhu, Jiaolong Yang, Zhibo Chen, Yao Mu, Xiaokang Yang, Xin Jin, Wenjun Zeng

OrganizationsEastern Institute of Technology, NingboHong Kong Polytechnic UniversityShanghai Jiao Tong UniversityTsinghua UniversityUniversity of Science and Technology of ChinaZhongguancun Academy

Why you should read this

Presents DeformGen, a dynamics-based augmentation framework that expands demonstration datasets for deformable object manipulation by forward-simulating physically plausible states and warping robot trajectories to match deformed geometries.

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.

Added

2026-09-30