Automating Deformable Gasket Assembly
This addresses a specific automation challenge in manufacturing industries like automotive and electronics, but is incremental as it compares existing methods without introducing a fundamentally new approach.
The paper tackled the problem of automating deformable gasket assembly, a high-precision task common in manufacturing, by comparing four methods including deep imitation learning and procedural algorithms, with results showing the Binary+ algorithm achieving 10/10 success on straight channels while the learned policy succeeded in 8/10 trials but was slower.
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/