29 citations · 62 across the 7 of their papers we have counts for
3 papers · 1 filter
Experimental Force-Torque Dataset for Robot Learning of Multi-Shape Insertion
Giovanni De Magistris, Asim Munawar, Tu-Hoa Pham +3
The accurate modeling of real-world systems and physical interactions is a common challenge towards the resolution of robotics tasks. Machine learning approaches have demonstrated…
Deep Reinforcement Learning for High Precision Assembly Tasks
Tadanobu Inoue, Giovanni De Magistris, Asim Munawar +2
High precision assembly of mechanical parts requires accuracy exceeding the robot precision. Conventional part mating methods used in the current manufacturing requires tedious tun…
Transfer learning from synthetic to real images using variational autoencoders for robotic applications
Tadanobu Inoue, Subhajit Chaudhury, Giovanni De Magistris +1
Robotic learning in simulation environments provides a faster, more scalable, and safer training methodology than learning directly with physical robots. Also, synthesizing images…