High precision grasp pose detection in dense clutter
| Autor: | Robert W. Platt, Marcus Gualtieri, Kate Saenko, Andreas ten Pas |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
TheoryofComputation_MISCELLANEOUS
FOS: Computer and information sciences 0209 industrial biotechnology business.industry Computer science GRASP 02 engineering and technology Computer Science - Robotics 020901 industrial engineering & automation Grippers 0202 electrical engineering electronic engineering information engineering Clutter Robot 020201 artificial intelligence & image processing Computer vision Artificial intelligence Representation (mathematics) Focus (optics) business Robotics (cs.RO) |
| Zdroj: | IROS |
| DOI: | 10.48550/arxiv.1603.01564 |
| Popis: | This paper considers the problem of grasp pose detection in point clouds. We follow a general algorithmic structure that first generates a large set of 6-DOF grasp candidates and then classifies each of them as a good or a bad grasp. Our focus in this paper is on improving the second step by using depth sensor scans from large online datasets to train a convolutional neural network. We propose two new representations of grasp candidates, and we quantify the effect of using prior knowledge of two forms: instance or category knowledge of the object to be grasped, and pretraining the network on simulated depth data obtained from idealized CAD models. Our analysis shows that a more informative grasp candidate representation as well as pretraining and prior knowledge significantly improve grasp detection. We evaluate our approach on a Baxter Research Robot and demonstrate an average grasp success rate of 93% in dense clutter. This is a 20% improvement compared to our prior work. |
| Databáze: | OpenAIRE |
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