3 citations · 3 across the 1 of their papers we have counts for
4 papers · 1 filter
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…
Imitation Learning-based Direct Visual Servoing using the Large Projection Formulation
Sayantan Auddy, Antonio Paolillo, Justus Piater +1
Today robots must be safe, versatile, and user-friendly to operate in unstructured and human-populated environments. Dynamical system-based imitation learning enables robots to per…
Continual Domain Randomization
Josip Josifovski, Sayantan Auddy, Mohammadhossein Malmir +3
Domain Randomization (DR) is commonly used for sim2real transfer of reinforcement learning (RL) policies in robotics. Most DR approaches require a simulator with a fixed set of tun…
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…