7 citations · 12 across the 5 of their papers we have counts for
5 papers
Seq2Seq Imitation Learning for Tactile Feedback-based Manipulation
Wenyan Yang, Alexandre Angleraud, Roel S. Pieters +2
Robot control for tactile feedback-based manipulation can be difficult due to the modeling of physical contacts, partial observability of the environment, and noise in perception a…
OpenDR: An Open Toolkit for Enabling High Performance, Low Footprint Deep Learning for Robotics
N. Passalis, S. Pedrazzi, R. Babuska +15
Existing Deep Learning (DL) frameworks typically do not provide ready-to-use solutions for robotics, where very specific learning, reasoning, and embodiment problems exist. Their r…
Monolithic vs. hybrid controller for multi-objective Sim-to-Real learning
Atakan Dag, Alexandre Angleraud, Wenyan Yang +4
Simulation to real (Sim-to-Real) is an attractive approach to construct controllers for robotic tasks that are easier to simulate than to analytically solve. Working Sim-to-Real so…
AR-based interaction for safe human-robot collaborative manufacturing
Antti Hietanen, Jyrki Latokartano, Roel Pieters +2
Industrial standards define safety requirements for Human-Robot Collaboration (HRC) in industrial manufacturing. The standards particularly require real-time monitoring and securin…
Object Pose Estimation in Robotics Revisited
Antti Hietanen, Jyrki Latokartano, Alessandro Foi +4
Vision based object grasping and manipulation in robotics require accurate estimation of object's 6D pose. The 6D pose estimation has received significant attention in computer vis…