Quasi-static Soft Fixture Analysis of Rigid and Deformable Objects
Yifei Dong, Florian T. Pokorny

TL;DR
This paper introduces a sampling-based method for estimating escape energy in caging-based manipulation of rigid and deformable 3D objects, extending energy constraints to include gravitational and elastic potential energies.
Contribution
It extends energy-bounded caging to a broader set of energy functions and provides a practical sampling approach for higher-dimensional configuration spaces.
Findings
Method produces rapidly converging upper-bound estimates of escape energy.
Simulations demonstrate applicability to complex manipulation scenarios.
Real-world experiments show strong correlation between escape energy estimates and object escape probability.
Abstract
We present a sampling-based approach to reasoning about the caging-based manipulation of rigid and a simplified class of deformable 3D objects subject to energy constraints. Towards this end, we propose the notion of soft fixtures extending earlier work on energy-bounded caging to include a broader set of energy function constraints and settings, such as gravitational and elastic potential energy of 3D deformable objects. Previous methods focused on establishing provably correct algorithms to compute lower bounds or analytically exact estimates of escape energy for a very restricted class of known objects with low-dimensional C-spaces, such as planar polygons. We instead propose a practical sampling-based approach that is applicable in higher-dimensional C-spaces but only produces a sequence of upper-bound estimates that, however, appear to converge rapidly to actual escape energy. We…
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Taxonomy
Topics3D Shape Modeling and Analysis · Robot Manipulation and Learning · Image Processing and 3D Reconstruction
