Leveraging Big Data for Grasp Planning


Conference Paper


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.

Author(s): Kappler, D. and Bohg, B. and Schaal, S.
Book Title: Proceedings of the IEEE International Conference on Robotics and Automation
Year: 2015
Month: May

Department(s): Autonomous Motion
Research Project(s): Learning to Grasp from Big Data
Bibtex Type: Conference Paper (inproceedings)
Paper Type: Conference

DOI: 10.1109/ICRA.2015.7139793
State: Published

Links: PDF


  title = {Leveraging Big Data for Grasp Planning},
  author = {Kappler, D. and Bohg, B. and Schaal, S.},
  booktitle = {Proceedings of the IEEE International Conference on Robotics and Automation},
  month = may,
  year = {2015},
  month_numeric = {5}