95 citations · 184 across the 32 of their papers we have counts for
4 papers · 1 filter
Learning and Extrapolation of Robotic Skills using Task-Parameterized Equation Learner Networks
Hector Villeda, Justus Piater, Matteo Saveriano
Imitation learning approaches achieve good generalization within the range of the training data, but tend to generate unpredictable motions when querying outside this range. We pre…
Improving the Trainability of Deep Neural Networks through Layerwise Batch-Entropy Regularization
David Peer, Bart Keulen, Sebastian Stabinger +2
Training deep neural networks is a very demanding task, especially challenging is how to adapt architectures to improve the performance of trained models. We can find that sometime…
Action Noise in Off-Policy Deep Reinforcement Learning: Impact on Exploration and Performance
Jakob Hollenstein, Sayantan Auddy, Matteo Saveriano +2
Many Deep Reinforcement Learning (D-RL) algorithms rely on simple forms of exploration such as the additive action noise often used in continuous control domains. Typically, the sc…
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…