2023/10/21 by Jungpyo Lee, Lee, Jungpyo, Sebastian Lee +5
Engineering · #FOS: Computer and information sciences #Robot Manipulation and Learning #Robotics (cs.RO) #Soft Robotics and Applications #Teleoperation and Haptic Systems
paper · pdf · doi:10.48550/arxiv.2401.06354
openalex publication_date 2023/10/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.