Supersizing self supervision: Learning to grasp from 50K tries and 700 robot hours
Current model free learning based robot grasping approaches exploit human labeled datasets for training the models. However, there are two problems with such a methodology: (a) since each object can be grasped in multiple ways, manually labeling grasp locations is not a trivial task; (b) human labeling is biased by semantics. While there have been attempts to train robots using trial and error experiments, the amount of data used in such experiments remains substantially low and hence makes the learner prone to over fitting. In this paper, we take the leap of increasing the available training data to 40 times more than prior work, leading to a dataset size of 50K data points collected over 700 hours of robot grasping attempts. This allows us to train a Convolutional Neural Network (CNN) for the task of predicting grasp locations without severe overfitting. In our formulation, we recast the regression problem to an 18 way binary classification over image patches. We also present a multi stage learning approach where a CNN trained in one stage is used to collect hard negatives in subsequent stages. Our experiments clearly show the benefit of using large scale datasets (and multi stage training) for the task of grasping. We also compare to several baselines and show state of the art performance on generalization to unseen objects for grasping.
2016 IEEE International Conference on Robotics and Automation (ICRA)