Generating Future Observations to Estimate Grasp Success in Cluttered
  Environments
Daniel Fernandes Gomes Wenxuan Mou Paolo Paoletti Shan Luo
End-to-end self-supervised models have been proposed for estimating the
success of future candidate grasps and video predictive models for generating
future observations. However, none have yet studied these two strategies
side-by-side for addressing the aforementioned grasping problem. We investigate
and compare a model-free approach, to estimate the success of a candidate
grasp, against a model-based alternative that exploits a self-supervised learnt
predictive model that generates a future observation of the gripper about to
grasp an object. Our experiments demonstrate that despite the end-to-end
model-free model obtaining a best accuracy of 72%, the proposed model-based
pipeline yields a significantly higher accuracy of 82%.