@misc{lee_initial_2023,
	title = {Initial {Analysis} of {Data}-{Driven} {Haptic} {Search} for the {Smart} {Suction} {Cup}},
	url = {http://arxiv.org/abs/2401.06354},
	doi = {10.48550/arXiv.2401.06354},
	abstract = {Suction cups offer a useful gripping solution, particularly in industrial robotics and warehouse applications. Vision-based grasp algorithms, like Dex-Net, show promise but struggle to accurately perceive dark or reflective objects, sub-resolution features, and occlusions, resulting in suction cup grip failures. In our prior work, we designed the Smart Suction Cup, which estimates the flow state within the cup and provides a mechanically resilient end-effector that can inform arm feedback control through a sense of touch. We then demonstrated how this cup's signals enable haptically-driven search behaviors for better grasping points on adversarial objects. This prior work uses a model-based approach to predict the desired motion direction, which opens up the question: does a data-driven approach perform better? This technical report provides an initial analysis harnessing the data previously collected. Specifically, we compare the model-based method with a preliminary data-driven approach to accurately estimate lateral pose adjustment direction for improved grasp success.},
	urldate = {2025-12-29},
	publisher = {arXiv},
	author = {Lee, Jungpyo and Lee, Sebastian D. and Huh, Tae Myung and Stuart, Hannah S.},
	month = oct,
	year = {2023},
	note = {arXiv:2401.06354 [cs]},
	keywords = {Computer Science - Robotics, ⭕/unread},
}

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