13 citations · 32 across the 7 of their papers we have counts for
8 papers
Machine Vision based Sample-Tube Localization for Mars Sample Return
Shreyansh Daftry, Barry Ridge, William Seto +8
A potential Mars Sample Return (MSR) architecture is being jointly studied by NASA and ESA. As currently envisioned, the MSR campaign consists of a series of 3 missions: sample cac…
Rover Relocalization for Mars Sample Return by Virtual Template Synthesis and Matching
Tu-Hoa Pham, William Seto, Shreyansh Daftry +11
We consider the problem of rover relocalization in the context of the notional Mars Sample Return campaign. In this campaign, a rover (R1) needs to be capable of autonomously navig…
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…
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…
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…
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…