Learning physics: extrapolation and learning equations

Learning physics: extrapolation and learning equations

In this project, we develop specific machine learning methods for identifying an underlying functional form that gave rise to data.

In particular for mechanical systems which are governed by the laws of physics, if we can learn the actual equations then accurate predictions outside of the domain of the available data can be made, i.e. we are able to extrapolate. Our current approach uses a modified feed-forward network with sine, cosine, identity and multiplicative units (with two terms) and a particular regularization and model selection scheme.


Preprint paper: G. Martius and C. H. Lampert. Extrapolation and learning equations. arXiv 1610.02995. [  arXiv ]
Self-exploration for tendon-driven robots

Self-exploration for tendon-driven robots

In order to test our self-organizing control framework on real and challenging systems, we apply our neural plasticity rule to tendon driven musculosceletal robots such as the Myorobotics arm-shoulder system.

 Check out the videos and the papers below.

Papers: G. Martius, R. Hostettler, A. Knoll, and R. Der. Compliant control for soft robots: emergent behavior of a tendon
G. Martius, R. Hostettler, A. Knoll, and R. Der. Compliant control for soft robots: emergent behavior of a tendon driven anthropomorphic arm. In Proc. IEEE Int. Conf. on Intelligent Robots and Systems (IROS), 2016. in press. [ bib | Supplementary page (videos) | pdf ]