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Robust 2D Assembly Sequencing via Geometric Planning with Learned Scores

Abstract

To compute robust 2D assembly plans, we present an approach that combines geometric planning with a deep neural network. We train the network using the Box2D physics simulator with added stochastic noise to yield robustness scores—the success probabilities of planned assembly motions. As running a simulation for every assembly motion is impractical, we train a convolutional neural network to map assembly operations, given as an image pair of the subassemblies before and after they are mated, to a robustness score. The neural network prediction is used within a planner to quickly prune out motions that are not robust. We demonstrate this approach on two-handed planar assemblies, where the motions are one-step translations. Results suggest that the neural network can learn robustness to plan robust sequences an order of magnitude faster than physics simulation.

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Figure: (Left) A robust assembly operation, which we find as part of the assembly sequencing, versus an operation that is not (Right), which we opt to avoid.

 

Links

  • Tzvika Geft, Aviv Tamar, Ken Goldberg, Dan Halperin
    Robust 2D Assembly Sequencing via Geometric Planning with Learned Scores
    In 2019 IEEE International Conference on Automation Science and Engineering (CASE 2019)
    [IEEE Xplore] [arXiv]

Contacts

Tzvika Geft http://acg.cs.tau.ac.il/people/tzvika-geft/tzvika-geft null
Dan Halperin http://acg.cs.tau.ac.il/danhalperin danha@post.tau.ac.il
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