20 citations · 44 across the 11 of their papers we have counts for
4 papers · 1 filter
Lessons Learned from Real-World Experiments with DyRET: the Dynamic Robot for Embodied Testing
Tønnes F. Nygaard, Jørgen Nordmoen, Charles P. Martin +1
Robots are used in more and more complex environments, and are expected to be able to adapt to changes and unknown situations. The easiest and quickest way to adapt is to change th…
An Interactive Musical Prediction System with Mixture Density Recurrent Neural Networks
Charles P Martin, Jim Torresen
This paper is about creating digital musical instruments where a predictive neural network model is integrated into the interactive system. Rather than predicting symbolic music (e…
Evolving Robots on Easy Mode: Towards a Variable Complexity Controller for Quadrupeds
Tønnes Frostad Nygaard, Charles Patrick Martin, Jim Torresen +1
The complexity of a legged robot's environment or task can inform how specialised its gait must be to ensure success. Evolving specialised robotic gaits demands many evaluations -…
How do Mixture Density RNNs Predict the Future?
Kai Olav Ellefsen, Charles Patrick Martin, Jim Torresen
Gaining a better understanding of how and what machine learning systems learn is important to increase confidence in their decisions and catalyze further research. In this paper, w…