A developer's oriented API powered by ROS 2.0 and Gazebo. Built on top of the HRIM. We make use of AI techniques to accelerate and enhance modules. Examples include the acceleration of sensor inference or the creation of power-conscious sources that allow us to estimate the consumption of different tasks and trajectories, even before executing them.
Aimed for researchers with interest in exploring how Deep Learning can empower robots. This layer provides a variety of techniques (mainly for Reinforcement Learning and for Supervised Learning) built on top basic primitives powered by TensorFlow. All these techniques connect with the underlying layer that interoperates with ROS. A roboticists' approach to AI.
User-oriented,, this layer aims to provide a simple yet complete set of functions to facilitate the use of robots. We research how AI can be used to enhance traditional path planning techniques and how robots can learn a given task through imitation.
Rather than programming, training allows robots to achieve behaviors that generalize better and are capable to respond to real-world needs. Using state of the art algorithms to explore and extend modular robots. Making the integration time and effort easy, and develop new behaviors.