Towards a Robust Soft Baby Robot With Rich Interaction Ability for
  Advanced Machine Learning Algorithms
Mohannad Alhakami Dylan R. Ashley Joel Dunham Francesco Faccio Eric Feron Jürgen Schmidhuber
Artificial intelligence has made great strides in many areas lately, yet it
has had comparatively little success in general-use robotics. We believe one of
the reasons for this is the disconnect between traditional robotic design and
the properties needed for open-ended, creativity-based AI systems. To that end,
we, taking selective inspiration from nature, build a robust, partially soft
robotic limb with a large action space, rich sensory data stream from multiple
cameras, and the ability to connect with others to enhance the action space and
data stream. As a proof of concept, we train two contemporary machine learning
algorithms to perform a simple target-finding task. Altogether, we believe that
this design serves as a first step to building a robot tailor-made for
achieving artificial general intelligence.