Automating Deformable Gasket Assembly
August 22, 2024 Β· Declared Dead Β· π 2024 IEEE 20th International Conference on Automation Science and Engineering (CASE)
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Authors
Simeon Adebola, Tara Sadjadpour, Karim El-Refai, Will Panitch, Zehan Ma, Roy Lin, Tianshuang Qiu, Shreya Ganti, Charlotte Le, Jaimyn Drake, Ken Goldberg
arXiv ID
2408.12593
Category
cs.RO: Robotics
Cross-listed
cs.CV
Citations
3
Venue
2024 IEEE 20th International Conference on Automation Science and Engineering (CASE)
Last Checked
4 months ago
Abstract
In Gasket Assembly, a deformable gasket must be aligned and pressed into a narrow channel. This task is common for sealing surfaces in the manufacturing of automobiles, appliances, electronics, and other products. Gasket Assembly is a long-horizon, high-precision task and the gasket must align with the channel and be fully pressed in to achieve a secure fit. To compare approaches, we present 4 methods for Gasket Assembly: one policy from deep imitation learning and three procedural algorithms. We evaluate these methods with 100 physical trials. Results suggest that the Binary+ algorithm succeeds in 10/10 on the straight channel whereas the learned policy based on 250 human teleoperated demonstrations succeeds in 8/10 trials and is significantly slower. Code, CAD models, videos, and data can be found at https://berkeleyautomation.github.io/robot-gasket/
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