Neural models have incredible learning and modeling capabilities which was demonstrated in complex robot learning tasks (e.g., Martin Riedmiller’s or Sergey Levine’s work). While these results are promising we lack a theoretical understanding of the learning capabilities of such networks and it is unclear how learned features and models can be reused or exploited in other tasks.
The ai-lab investigates deep neural network implementations that are theoretical grounded in the framework of probabilistic inference and develops deep transfer learning strategies for stochastic neural networks. We evaluate our models in challenging robotics applications where the networks have to scale to high-dimensional control signals and need to generate reactive feedback command in real-time.
Our developments will enable complex online adaptation and skill learning behavior in autonomous systems and will help to gain a better understanding of the meaning and function of the learned features in large neural networks with millions of parameters.