We train robots to solve general tasks using only images. We present the robot with an image of its desired goal configuration, and it learns to reach that goal image using only images of the environment.
The goal of this project is to levitate a group of robots in 3D space using electromagnetic energy. MagLev (magnetically levitated) robots, providing frictionless motions and precise motion control, have promising potential applications in many fields. Controlling magnetic levitation systems is not an easy task; therefore, designing a robust controller is crucial for accurate manipulations in the 3D space and to allow the robots to reach any desired location smoothly.
The goal of this work is to develop methods and tools which will enable reliable dynamic balance and gait for bipedal robots. Currently, the focus of this project is on the development and application of methods for quantifying and measuring various kinematic and dynamic properties of the robot which are critical to dynamic bipedal gait and balance.
The main motivation for this project is to improve the model of dynamical systems, beyond the physics-based models, by incorporating measurements of the model outputs/states in the modelling process. This method of modelling is called Grey-Box which employs a White-Box model (physics-based model) in conjunction with a Black-Box model (purely based on data).
This work presents a path following controller for a quadrotor vehicle. A smooth, dynamic, feedback controller is designed that allows the quadrotor to follow both closed and non-closed embedded curves while maintaining a desired speed, a desired acceleration or while stabilizing desired points along the curves.