This framework demonstrates improved robotic assembly success by addressing the simulation-to-reality gap, highlighting adaptability in complex environments.
Key Points
The proposed method improves peg-in-hole assembly success by 75%, overcoming traditional programming limitations.
It employs a U-net segmentation model, achieving 15 successful assembly tests out of 20 in real-world applications.
Integration of visual and force feedback enhances precision in insertion tasks across varying light conditions.
Evaluation includes simulations and real robotic environments, indicating robust performance despite challenges.