Generating Future Observations to Estimate Grasp Success in Cluttered Environments
<|reference_start|>Generating Future Observations to Estimate Grasp Success in Cluttered Environments: 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%.<|reference_end|>
@article{gomes2023generating,
    title={Generating Future Observations to Estimate Grasp Success in Cluttered
  Environments},
    author={Daniel Fernandes Gomes, Wenxuan Mou, Paolo Paoletti and Shan Luo},
    journal={5th UK Robot Manipulation Workshop 2024},
    year={2023},
    archivePrefix={arXiv},
    eprint={2403.07877},
    primaryClass={cs.RO cs.CV}
}