Convolutional Occupancy Models for Dense Packing of Complex, Novel Objects

• URL: http://arxiv.org/abs/2308.00091v1
• Date: Mon, 31 Jul 2023 19:08:16 GMT
• Title: Convolutional Occupancy Models for Dense Packing of Complex, Novel Objects
• Authors: Nikhil Mishra, Pieter Abbeel, Xi Chen, Maximilian Sieb
• Abstract summary: We present a fully-convolutional shape completion model, F-CON, that can be easily combined with off-the-shelf planning methods for dense packing in the real world. We also release a simulated dataset, COB-3D-v2, that can be used to train shape completion models for real-word robotics applications. Finally, we equip a real-world pick-and-place system with F-CON, and demonstrate dense packing of complex, unseen objects in cluttered scenes.
• Score: 75.54599721349037
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Dense packing in pick-and-place systems is an important feature in many warehouse and logistics applications. Prior work in this space has largely focused on planning algorithms in simulation, but real-world packing performance is often bottlenecked by the difficulty of perceiving 3D object geometry in highly occluded, partially observed scenes. In this work, we present a fully-convolutional shape completion model, F-CON, which can be easily combined with off-the-shelf planning methods for dense packing in the real world. We also release a simulated dataset, COB-3D-v2, that can be used to train shape completion models for real-word robotics applications, and use it to demonstrate that F-CON outperforms other state-of-the-art shape completion methods. Finally, we equip a real-world pick-and-place system with F-CON, and demonstrate dense packing of complex, unseen objects in cluttered scenes. Across multiple planning methods, F-CON enables substantially better dense packing than other shape completion methods.

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