  Learning Machines is developing a flexible, cross-industry, advanced
analytics platform, targeted during stealth-stage at a limited number of
specific vertical applications. In this paper, we aim to integrate a general
machine system to learn a variant of tasks from simulation to real world. In
such a machine system, it involves real-time robot vision, sensor fusion, and
learning algorithms (reinforcement learning). To this end, we demonstrate the
general machine system on three fundamental tasks including obstacle avoidance,
foraging, and predator-prey robot. The proposed solutions are implemented on
Robobo robots with mobile device (smartphone with camera) as interface and
built-in infrared (IR) sensors. The agent is trained in a virtual environment.
In order to assess its performance, the learned agent is tested in the virtual
environment and reproduce the same results in a real environment. The results
show that the reinforcement learning algorithm can be reliably used for a
variety of tasks in unknown environments.
