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.
Wearable computer vision and deep learning are combined for real-time sensing and classification of human walking environments. Applications of this research include optimal path planning, obstacle avoidance, and environment-adaptive control of robotic exoskeletons. These powered biomechatronic devices provide assistance to individuals with mobility impairments resulting from ageing and/or physical disabilities.
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.
This project focuses on deploying a set of autonomous robots to efficiently service tasks that arrive sequentially in an environment over time. Each task is serviced when the robot visits the corresponding task location. Robots can then redeploy while waiting for the next task to arrive. The objective is to redeploy the robots taking into account the expected response time to service tasks that will arrive in the future.