29 citations · 94 across the 14 of their papers we have counts for
6 papers · 1 filter
Deep Learning with Predictive Control for Human Motion Tracking
Don Joven Agravante, Giovanni De Magistris, Asim Munawar +2
We propose to combine model predictive control with deep learning for the task of accurate human motion tracking with a robot. We design the MPC to allow switching between the lear…
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…
MaestROB: A Robotics Framework for Integrated Orchestration of Low-Level Control and High-Level Reasoning
Asim Munawar, Giovanni De Magistris, Tu-Hoa Pham +5
This paper describes a framework called MaestROB. It is designed to make the robots perform complex tasks with high precision by simple high-level instructions given by natural lan…
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…
OptLayer - Practical Constrained Optimization for Deep Reinforcement Learning in the Real World
Tu-Hoa Pham, Giovanni De Magistris, Ryuki Tachibana
While deep reinforcement learning techniques have recently produced considerable achievements on many decision-making problems, their use in robotics has largely been limited to si…