6 citations · 18 across the 9 of their papers we have counts for
4 papers · 1 filter
Unsupervised Learning of Effective Actions in Robotics
Marko Zaric, Jakob Hollenstein, Justus Piater +1
Learning actions that are relevant to decision-making and can be executed effectively is a key problem in autonomous robotics. Current state-of-the-art action representations in ro…
Scalable and Efficient Continual Learning from Demonstration via a Hypernetwork-generated Stable Dynamics Model
Sayantan Auddy, Jakob Hollenstein, Matteo Saveriano +2
Robots capable of learning from demonstration (LfD) must exhibit stability while executing learned motion skills. To be effective in the real world, they should also remember multi…
Differentiable Forward Kinematics for TensorFlow 2
Lukas Mölschl, Jakob J. Hollenstein, Justus Piater
Robotic systems are often complex and depend on the integration of a large number of software components. One important component in robotic systems provides the calculation of for…
Continual Learning from Demonstration of Robotics Skills
Sayantan Auddy, Jakob Hollenstein, Matteo Saveriano +2
Methods for teaching motion skills to robots focus on training for a single skill at a time. Robots capable of learning from demonstration can considerably benefit from the added a…