The field of robotics has been undergoing a major change from manufacturing applications to entertainment, home, rehabilitation, search and rescue, and service applications.

Although robots seem to possess fantastic skills in science fiction and movies, most people would be surprised to learn how much remains to be accomplished to provide today's robots with the ability to do relatively simple tasks.

Autonomous robots are only able to complete very simple tasks within limited environmental conditions. Humans can be incorporated to teleoperate or supervise robots, but as the robot complexity increases so does the human's workload. Robotics requires research in many areas that include hybrid systems, embedded systems, sensory fusion, distributed artificial intelligence, computer vision, machine learning, human-machine interaction, localization, planning, navigation, etc. This large field provides ample research problems.

The Engineering School's Department of Electrical Engineering and Computer Science houses the Centre for Intelligent Systems (CIS) that encompasses both the Cognitive Robotics Lab (CRL) and the Intelligent Robotics Lab (IRL).

In addition to CIS, the department also includes six addition laboratories that conduct robotics research: the Computational Cognitive Neuroscience Laboratory (CCN), the Embedded Computing Systems Laboratory (ECS), the Embedded and Hybrid Systems Laboratory (EHS), the Human-Machine Teaming Laboratory (HMT), the Modelling and Analysis of Complex Systems (MACS) group, and the Robotics and Autonomous Systems Laboratory (RAS).

Each individual laboratory provides a specific robotics research focus. The broad research areas include: biologically inspired robotic control (CCNL), cognitive robotics (CRL), embedded systems (ECS, EHS), human-robotic interaction (HMT, IRL, RAS), humanoid robotics (CRL), planning (MACS), sensor networks (EHS), hybrid robotic systems (EHS, MACS), mobile robot navigation (IRL), multiple robot coordination and cooperation (HMT), real-time systems (EHS), and rehabilitation robotics (RAS).

Topics in this research:

• Biologically inspired robot control
• Decision-Theoretic planning and control
• Humanoid robots
• Human-robot interaction
• Hybrid and Distributed Control
• Knowledge sharing among robots
• Mobile robot navigation
• Mobile sensor networks
• Modeling, simulation and diagnosis
• Multiple robot coordination and cooperation
• Personal and service robots
• Range-free perception-based navigation
• Rehabilitation robotics
• Sensory EgoSphere
• Stochastic hybrid systems for multiple robot teams
• Vision, image and signal processing systems

Multimodal open-domain conversations with robotic platforms:

The interaction environment consists not only of the immediate social context of the conversations but also of the physical environment in which the human and the robot agent operate. Social interaction takes place via natural language communication usually in face-to-face situations, and traditionally this environment has been a separate context for dialog modeling updated according to the agents' dialog acts. Due to advances in IoT technology, the context for human–robot interaction has widened to include the physical and digital environment in which human–human and human–robot interactions occur.

The use of multiple robots from different manufacturers, using different operating software, requires a common framework such as ROS (Robot Operating System). Some robots (such as Nao) have their own operating software (Naoqi on Nao) but can be connected to ROS via a ROS-Naoqi bridge module. Some robots (such as Care-O-Bot 4) use ROS directly. ROS can also be connected to IoT via a ROS-IoT bridge module.