3 citations · 14 across the 8 of their papers we have counts for
14 papers
PARTNR: Pick and place Ambiguity Resolving by Trustworthy iNteractive leaRning
Jelle Luijkx, Zlatan Ajanovic, Laura Ferranti +1
Several recent works show impressive results in mapping language-based human commands and image scene observations to direct robot executable policies (e.g., pick and place poses).…
Interactive Imitation Learning in Robotics: A Survey
Carlos Celemin, Rodrigo Pérez-Dattari, Eugenio Chisari +7
Interactive Imitation Learning (IIL) is a branch of Imitation Learning (IL) where human feedback is provided intermittently during robot execution allowing an online improvement of…
Solving Robot Assembly Tasks by Combining Interactive Teaching and Self-Exploration
Mariano Ramirez Montero, Giovanni Franzese, Jeroen Zwanepol +1
Many high precision (dis)assembly tasks are still being performed by humans, whereas this is an ideal opportunity for automation. This paper provides a framework which enables a no…
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…
Learning Task-Parameterized Skills from Few Demonstrations
Jihong Zhu, Michael Gienger, Jens Kober
Moving away from repetitive tasks, robots nowadays demand versatile skills that adapt to different situations. Task-parameterized learning improves the generalization of motion pol…
ILoSA: Interactive Learning of Stiffness and Attractors
Giovanni Franzese, Anna Mészáros, Luka Peternel +1
Teaching robots how to apply forces according to our preferences is still an open challenge that has to be tackled from multiple engineering perspectives. This paper studies how to…