Skip to main content Skip to main navigation


Online Learning with Stochastic Recurrent Neural Networks using Intrinsic Motivation Signals

Daniel Tanneberg; Jan Peters; Elmar Rueckert
In: Proceedings of the 1st Annual Conference on Robot Learning. Conference on Robot Learning (CoRL-2017), November 13-15, Mountain View, California, USA, Pages 167-174, Proceedings of Machine Learning Research, Vol. 78, PMLR, 2017.


Continuous online adaptation is an essential ability for the vision of fully autonomous and lifelong-learning robots. Robots need to be able to adapt to changing environments and constraints while this adaption should be performed without interrupting the robot’s motion. In this paper, we introduce a framework for probabilistic online motion planning and learning based on a bio-inspired stochastic recurrent neural network. Furthermore, we show that the model can adapt online and sample-efficiently using intrinsic motivation signals and a mental replay strategy. This fast adaptation behavior allows the robot to learn from only a small number of physical interactions and is a promising feature for reusing the model in different environments. We evaluate the online planning with a realistic dynamic simulation of the KUKA LWR robotic arm. The efficient online adaptation is shown in simulation by learning an unknown workspace constraint using mental replay andtextitcognitive dissonance as intrinsic motivation signal.

Weitere Links