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Lattice-based shape tracking and servoing of elastic objects

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arxiv 2209.01832 v3 pith:3XOGPADL submitted 2022-09-05 cs.RO

classification cs.RO
keywords approachobjectobjectsdeformablelatticeshapecloudcomplexity
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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  1. DefFusionNet: Learning Multimodal Goal Shapes for Deformable Object Manipulation via a Diffusion-based Probabilistic Model

    cs.RO 2025-06 conditional novelty 6.0 of 10

    DefFusionNet learns a conditional diffusion model over goal point clouds for deformable shape servoing, enabling diverse multimodal goals and outperforming DefGoalNet with fewer demonstrations.

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