Daniel Tanneberg, Jan Peters, Elmar Rueckert
Intrinsic Motivation and Mental Replay enable Efficient Online Adaptation in Stochastic Recurrent Networks
accepted (Oct, 9th 2018) at Neural Networks – Elsevier with an Impact Factor of 7.197 (2017).
Daniel Tanneberg, Jan Peters, Elmar Rueckert
Intrinsic Motivation and Mental Replay enable Efficient Online Adaptation in Stochastic Recurrent Networks
accepted (Oct, 9th 2018) at Neural Networks – Elsevier with an Impact Factor of 7.197 (2017).
Gondaliya, D. Kaushikkumar; Peters, J.; Rueckert, E. (2018). Learning to categorize bug reports with LSTM networks: An empirical study on thousands of real bug reports from a world leading software company, Proceedings of the International Conference on Advances in System Testing and Validation Lifecycle (VALID).
Adrian Šošić, Elmar Rueckert, Jan Peters, Abdelhak M. Zoubir, Heinz Koeppl
Inverse Reinforcement Learning via Nonparametric Spatio-Temporal Subgoal Modeling
Title: Experience Replay and Intrinsic Motivation in Neural Motor Skill Learning Models
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.
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).
With February 1st, 2018 I will work as professor for robotics at the university Lübeck.
Title: Neural models for robot motor skill learning.
Abstract:
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.