Abstract
Suction cup grasping is very common in industry, but moving too quickly can cause suction cups to detach, causing drops or damage. Maintaining a suction grasp throughout a high-speed motion requires balancing suction forces against inertial forces while the suction cups deform under strain. In this paper, we consider Grasp Optimized Motion Planning for Suction Transport (GOMP-ST), an algorithm that combines deep learning with optimization to decrease transport time while avoiding suction cup failure. GOMP-ST first repeatedly moves a physical robot, vacuum gripper, and a sample object, while measuring pressure with a solid-state sensor to learn critical failure conditions. Then, these are integrated as constraints on the accelerations at the end-effector into a time-optimizing motion planner. The resulting plans incorporate real-world effects such as suction cup deformation that are difficult to model analytically. In GOMP-ST, the learned constraint, modeled with a neural network, is linearized using Autograd and integrated into a sequential quadratic program optimization. In 420 experiments with a physical UR5 transporting objects ranging from 1.3 to 1.7 kg, we compare GOMP-ST to baseline optimizing motion planners. Results suggest that GOMP-ST can avoid suction cup failure while decreasing transport times from 16 to 58%. For code, video, and datasets, see https://sites.google.com/view/gomp-st
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Acknowledgments
This research was performed at the AUTOLAB at UC Berkeley in affiliation with the Berkeley AI Research (BAIR) Lab, Berkeley Deep Drive (BDD), the Real-Time Intelligent Secure Execution (RISE) Lab, and the CITRIS “People and Robots” (CPAR) Initiative. We thank our colleagues for their helpful feedback and suggestions. We thank Tae Myung Huh and Michael Danielczuk for their invaluable advice. We thank Adam Lau for his professional photography. We thank Adam Rashid for his help running the physical robot. This article solely reflects the opinions and conclusions of its authors and do not reflect the views of the sponsors or their associated entities.
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Yahav Avigal and Jeffrey Ichnowski–Equal contribution
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Avigal, Y., Ichnowski, J., Cao, M.Y., Goldberg, K. (2023). GOMP-ST: Grasp Optimized Motion Planning for Suction Transport. In: LaValle, S.M., O’Kane, J.M., Otte, M., Sadigh, D., Tokekar, P. (eds) Algorithmic Foundations of Robotics XV. WAFR 2022. Springer Proceedings in Advanced Robotics, vol 25. Springer, Cham. https://doi.org/10.1007/978-3-031-21090-7_29
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DOI: https://doi.org/10.1007/978-3-031-21090-7_29
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