activity
20212024
most citedPseudo-Haptics Survey: Human-Computer Interaction in Extended Reality & Teleoperation

20 citations · 24 across the 6 of their papers we have counts for

collaborators

6 papers

astro-ph.IM2024

Optimal Attitude Control of Large Flexible Space Structures with Distributed Momentum Actuators

Pedro Cachim, Will Kraus, Zachary Manchester +2

Recent spacecraft mission concepts propose larger payloads that have lighter, less rigid structures. For large lightweight structures, the natural frequencies of their vibration mo…

cs.HC202420 cited

Pseudo-Haptics Survey: Human-Computer Interaction in Extended Reality & Teleoperation

Rui Xavier, José Luís Silva, Rodrigo Ventura +1

Pseudo-haptic techniques are becoming increasingly popular in human-computer interaction. They replicate haptic sensations by leveraging primarily visual feedback rather than mecha…

eess.SY20233 cited

A Biologically-Inspired Computational Model of Time Perception

Inês Lourenço, Robert Mattila, Rodrigo Ventura +1

Time perception - how humans and animals perceive the passage of time - forms the basis for important cognitive skills such as decision-making, planning, and communication. In this…

cs.LG20221 cited

Symplectic Momentum Neural Networks -- Using Discrete Variational Mechanics as a prior in Deep Learning

Saul Santos, Monica Ekal, Rodrigo Ventura

With deep learning gaining attention from the research community for prediction and control of real physical systems, learning important representations is becoming now more than e…

cs.RO2021

Online Information-Aware Motion Planning with Inertial Parameter Learning for Robotic Free-Flyers

Monica Ekal, Keenan Albee, Brian Coltin +3

Space free-flyers like the Astrobee robots currently operating aboard the International Space Station must operate with inherent system uncertainties. Parametric uncertainties like…

math.OC2021

COSMIC: fast closed-form identification from large-scale data for LTV systems

Maria Carvalho, Claudia Soares, Pedro Lourenço +1

We introduce a closed-form method for identification of discrete-time linear time-variant systems from data, formulating the learning problem as a regularized least squares problem…