Lattice-based shape tracking and servoing of elastic objects
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Abstract (arXiv)
In this paper, we propose a general unified tracking-servoing approach for controlling the shape of elastic deformable objects using robotic arms. Our approach works by forming a lattice around the object, binding the object to the lattice, and tracking and servoing the lattice instead of the object. This makes our approach have full 3D control over deformable objects of any general form (linear, thin-shell, volumetric). Furthermore, it decouples the runtime complexity of the approach from the objects' geometric complexity. Our approach is based on the As-Rigid-As-Possible (ARAP) deformation model. It requires no mechanical parameter of the object to be known and can drive the object toward desired shapes through large deformations. The inputs to our approach are the point cloud of the object's surface in its rest shape and the point cloud captured by a 3D camera in each frame. Overall, our approach is more broadly applicable than existing approaches. We validate the efficiency of our approach through numerous experiments with deformable objects of various shapes and materials (paper, rubber, plastic, foam). Experiment videos are available on the project website: https://sites.google.com/view/tracking-servoing-approach.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 5 Sep 2022.
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Citing papers per year: 2023: 2; 2024: 2; 2025: 1.
5 papers.
- 2024Differentiable Physics-based System Identification for Robotic Manipulation of Elastoplastic Materialscited by 6
- 2023DeformerNet: Learning Bimanual Manipulation of 3D Deformable Objectscited by 4
- 2023DefGoalNet: Contextual Goal Learning from Demonstrations For Deformable Object Manipulationcited by 3
- 2024Offline Goal-Conditioned Reinforcement Learning for Shape Control of Deformable Linear Objectscited by 1
- 2025DiffDef: A Diffusion Model for Generating Multimodal Goal Shapes From Demonstrations for Deformable Object Manipulationcited by 0
- 2025DiffDef: A Diffusion Model for Generating Multimodal Goal Shapes From Demonstrations for Deformable Object Manipulation23 Jun 2025
- 2024Differentiable Physics-based System Identification for Robotic Manipulation of Elastoplastic Materials1 Nov 2024
- 2024Offline Goal-Conditioned Reinforcement Learning for Shape Control of Deformable Linear Objects15 Mar 2024
- 2023DefGoalNet: Contextual Goal Learning from Demonstrations For Deformable Object Manipulation25 Sep 2023
- 2023DeformerNet: Learning Bimanual Manipulation of 3D Deformable Objects8 May 2023