29 citations · 94 across the 14 of their papers we have counts for
14 papers
Controllable Image Synthesis of Industrial Data Using Stable Diffusion
Gabriele Valvano, Antonino Agostino, Giovanni De Magistris +2
Training supervised deep neural networks that perform defect detection and segmentation requires large-scale fully-annotated datasets, which can be hard or even impossible to obtai…
Combining Thermodynamics-based Model of the Centrifugal Compressors and Active Machine Learning for Enhanced Industrial Design Optimization
Shadi Ghiasi, Guido Pazzi, Concettina Del Grosso +2
The design process of centrifugal compressors requires applying an optimization process which is computationally expensive due to complex analytical equations underlying the compre…
Deep Surrogate of Modular Multi Pump using Active Learning
Malathi Murugesan, Kanika Goyal, Laure Barriere +3
Due to the high cost and reliability of sensors, the designers of a pump reduce the needed number of sensors for the estimation of the feasible operating point as much as possible.…
Constrained Exploration and Recovery from Experience Shaping
Tu-Hoa Pham, Giovanni De Magistris, Don Joven Agravante +3
We consider the problem of reinforcement learning under safety requirements, in which an agent is trained to complete a given task, typically formalized as the maximization of a re…
Reinforcement Learning Testbed for Power-Consumption Optimization
Takao Moriyama, Giovanni De Magistris, Michiaki Tatsubori +3
Common approaches to control a data-center cooling system rely on approximated system/environment models that are built upon the knowledge of mechanical cooling and electrical and…
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…
Transfer Learning From Synthetic To Real Images Using Variational Autoencoders For Precise Position Detection
Tadanobu Inoue, Subhajit Chaudhury, Giovanni De Magistris +1
Capturing and labeling camera images in the real world is an expensive task, whereas synthesizing labeled images in a simulation environment is easy for collecting large-scale imag…
Focusing on What is Relevant: Time-Series Learning and Understanding using Attention
Phongtharin Vinayavekhin, Subhajit Chaudhury, Asim Munawar +4
This paper is a contribution towards interpretability of the deep learning models in different applications of time-series. We propose a temporal attention layer that is capable of…
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…
Limiting the Reconstruction Capability of Generative Neural Network using Negative Learning
Asim Munawar, Phongtharin Vinayavekhin, Giovanni De Magistris
Generative models are widely used for unsupervised learning with various applications, including data compression and signal restoration. Training methods for such systems focus on…