Title: Experience Replay and Intrinsic Motivation in Neural Motor Skill Learning Models
Archives
3 HUMANOIDS Papers Accepted
Rueckert, E.; Nakatenus, M.; Tosatto, S.; Peters, J. (2017). Learning Inverse Dynamics Models in O(n) time with LSTM networks.
Tanneberg, D.; Peters, J.; Rueckert, E. (2017). Efficient Online Adaptation with Stochastic Recurrent Neural Networks.
Stark, S.; Peters, J.; Rueckert, E. (2017). A Comparison of Distance Measures for Learning Nonparametric Motor Skill Libraries.
CoRL Paper accepted
Tanneberg, D.; Peters, J.; Rueckert, E. (2017). Online Learning with Stochastic Recurrent Neural Networks using Intrinsic Motivation Signals, Proceedings of the Conference on Robot Learning (CoRL).
W1 Juniorprofessorship with tenure track at University Lübeck
With February 1st, 2018 I will work as professor for robotics at the university Lübeck.
Invited Talk at University Lübeck
Title: Neural models for robot motor skill learning.
Abstract:
Invited Talk at the Frankfurt Institute for Advanced Studies (FIAS), Germany
Abstract: Movement planing is a fundamental skill that is involved in many human motor control tasks. While the hippocampus plays a central role, the functional principles underlying planning are largely unexplored. In this talk, I present a computational model for planning that is derived from theoretical principles of the probabilistic inference framework. Optimal learning rules are inferred and links to the widely used machine learning techniques expectation maximization and policy search are established. As computational model for hippocampal sweeps, we show that the network dynamics are qualitatively similar to transient firing patterns during planning and foraging in the hippocampus of awake behaving rats. In robotic tasks, non-Gaussian hard constraints are modeled, dozens of movement plans are simulated in parallel, and forward and inverse kinematic models are learned simultaneously through interactions with the environment.
Invited Talk at the Institute of Neuroinformatics (INI), Zurich, Switzerland
Probabilistic computational models of human motor control for robot learning.
Invited Talk at the Albert-Ludwigs-Universität Freiburg, Germany
Neural models for brain-machine interfaces and anthropomorphic robotics
Journal Paper Accepted at Nature Publishing Group: Scientific Reports.
Probabilistic Movement Models Show that Postural Control Precedes and Predicts Volitional Motor Control
Nature Publishing Group: Scientific Reports, 6 (28455), 2016.
Journal Paper Accepted at Nature Publishing Group: Scientific Reports.
Recurrent Spiking Networks Solve Planning Tasks
Nature Publishing Group: Scientific Reports, 6 (21142), 2016.