37 citations · 142 across the 32 of their papers we have counts for
9 papers · 1 filter
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).…
Benchmark for Models Predicting Human Behavior in Gap Acceptance Scenarios
Julian Frederik Schumann, Jens Kober, Arkady Zgonnikov
Autonomous vehicles currently suffer from a time-inefficient driving style caused by uncertainty about human behavior in traffic interactions. Accurate and reliable prediction mode…
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…
Interactive Imitation Learning of Bimanual Movement Primitives
Giovanni Franzese, Leandro de Souza Rosa, Tim Verburg +2
Performing bimanual tasks with dual robotic setups can drastically increase the impact on industrial and daily life applications. However, performing a bimanual task brings many ch…
Learning from Demonstrations of Critical Driving Behaviours Using Driver's Risk Field
Yurui Du, Flavia Sofia Acerbo, Jens Kober +1
In recent years, imitation learning (IL) has been widely used in industry as the core of autonomous vehicle (AV) planning modules. However, previous IL works show sample inefficien…
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…
Learning to Exploit Elastic Actuators for Quadruped Locomotion
Antonin Raffin, Daniel Seidel, Jens Kober +3
Spring-based actuators in legged locomotion provide energy-efficiency and improved performance, but increase the difficulty of controller design. While previous work has focused on…
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…