29 citations · 86 across the 23 of their papers we have counts for
8 papers · 1 filter
Injective State-Image Mapping facilitates Visual Adversarial Imitation Learning
Subhajit Chaudhury, Daiki Kimura, Asim Munawar +1
The growing use of virtual autonomous agents in applications like games and entertainment demands better control policies for natural-looking movements and actions. Unlike the conv…
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…
Safe Exploration in Markov Decision Processes with Time-Variant Safety using Spatio-Temporal Gaussian Process
Akifumi Wachi, Hiroshi Kajino, Asim Munawar
In many real-world applications (e.g., planetary exploration, robot navigation), an autonomous agent must be able to explore a space with guaranteed safety. Most safe exploration a…
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…
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…