Reviews: Robot Learning in Homes: Improving Generalization and Reducing Dataset Bias

In this paper a new dataset for robot grasping task is proposed. Compared to grasping data collected in a lab environment, the authors propose to collect the data from real world environments (homes). To collect data in the wild, the authors propose to use cheap robots (measured by the cost) with low DoF. In order to compensate the noisy behavior of the less calibrated robots, the authors model the noise as a latent variable and jointly learn it with the grasping task. Results show that the combination of the aforementioned ideas result in a robot grasping model that can work well on both lab environments, and new real world environment.