Robotics Research Center

Rational: We view the autonomous system as a machine (or a collection of such) that can sense the environment and change its behavior accordingly in order to accomplish some goals. 

To that end, the systems should be able to sense, analyze and interact with the environment in which they operate. 

Sensing of the environment includes a range of devices – in some cases described as IOT – the Internet of Things. Reasoning about the data in view of the goals includes methods of artificial intelligence, deep learning, and visual processing.

While interaction with the environment includes control and kinematics, mechatronics, energy (power cells), dynamics, mechanics and more.

The integration between the motor skills and the sensing and analysis with the mechanical interaction is done by communication methodologies.
Both novel sensing and new interaction modalities require material processing.

Method: The topics that cover this philosophical abstraction of robots we would like to explore include:  Scientific foundations, applications, and analysis of robotic systems.

Science and the Systems of Robotics:

Field Robotics: Underwater Robotics, Aerial/Space Robotics, Agricultural and Mining Robotics, Building and Construction, Environmental Monitoring, Autonomous Vehicles
Mechanisms and Design: Humanoids, Hands, Legged Systems, Snake Robots, Novel Actuators, Reconfigurable Robots, MEMS/NEMS, Micro/Nanobots, Novel Sensors, Haptics, Tactile Interfaces, Soft Robots, Wearable Robots, Design Optimization of Robotic Systems, Minimality in design.

Sensors and Machine Learning: Sensing devices, optical, mechanical, acoustic, electro-magnetic, etc. Robot Vision: Acquisition, analyzing, learning and understanding of visual information. 

Analysis of geometric structures, differential/metric integral geometry. Computational invariants.

Data vs. models (axiomatic vs learning). Autonomous Medicine:  Computational Anatomy, Man-machine interaction – reading emotions and motions, gestures, expressions, object recognition.

We wish in 5-7 years to be Leaders in theory and practice of learning methodologies integrated into real time control of mechanical systems.

This includes: Visual control of mechanical systems (operating in air, land, and sea, as well as within the human body, and other macro and micro environments). Remote/autonomous surgery. Computational analysis of medical information. Robotic surgery and Autonomous vehicles.