|Reference Type||Conference Proceedings|
|Author(s)||Daniel Kappler, Jeannette Bohg and Stefan Schaal|
|Title||Leveraging Big Data for Grasp Planning|
|Journal/Conference/Book Title||Proceedings of the IEEE International Conference on Robotics and Automation|
|Keywords||Data-Driven Grasp Synthesis, Deep Learning, Crowdsourcing|
|Abstract||We propose a new large-scale database containing grasps that are applied to a large set of objects from numerous categories. These grasps are generated in simulation and are annotated with different grasp stability metrics. We use a descriptive and efficient representation of the local object shape at which each grasp is applied. Given this data, we present a two-fold analysis: (i) We use crowdsourcing to analyze the correlation of the metrics with grasp success as predicted by humans. The results show that the metric based on physics simulation is a more consistent predictor for grasp success than the standard ε-metric. The results also support the hypothesis that human labels are not required for good ground truth grasp data. Instead the physics-metric can be used to generate datasets in simulation that may then be used to bootstrap learning in the real world. (ii) We apply a deep learning method and show that it can better leverage the large-scale database for prediction of grasp success compared to logistic regression. Furthermore, the results suggest that labels based on the physics-metric are less noisy than those from the ε-metric and therefore lead to a better classification performance.
|Link to PDF||http://www-amd.is.tuebingen.mpg.de/~bohg/2015_ICRA_kbs.pdf|