2 papers
cs.RO2025
Towards High Precision: An Adaptive Self-Supervised Learning Framework for Force-Based Verification
Zebin Duan, Frederik Hagelskjær, Aljaz Kramberger +2
The automation of robotic tasks requires high precision and adaptability, particularly in force-based operations such as insertions. Traditional learning-based approaches either re…
cs.RO2025
A General Peg-in-Hole Assembly Policy Based on Domain Randomized Reinforcement Learning
Xinyu Liu, Aljaz Kramberger, Leon Bodenhagen
Generalization is important for peg-in-hole assembly, a fundamental industrial operation, to adapt to dynamic industrial scenarios and enhance manufacturing efficiency. While prior…