Dr Claudio Zito is specialized in Cognitive Robotics, which entails developing robots capable of understanding scenes and making high-level decisions based on contextual information, essential for developing robots that not only perform tasks but also adapt their strategies based on their environment and experiences. Dr Claudio Zito’s expertise includes probabilistic robotics, robot planning and learning, knowledge representation and reasoning, decision-making process, reinforcement learning, learning from demonstration, and generative models.

One of his primary research areas is robot manipulation, where he combines mathematical modeling with artificial intelligence (AI) and machine learning (ML) techniques to enhance the operation of autonomous systems in uncertain and hazardous environments.

Dr. Zito is also interested in developing models for physical human-robot interaction (pHRI) to enhance remote tele-operation of robots that need to physically interact with a complex environment (e.g. inspection, nuclear waste disposal), or for wearable robotics, such as prostheses or exoskeleton, to provide super-human performance to operators.

Dr. Claudio Zito is an Assistant Professor at the School of Mathematical and Computer Science at Heriot-Watt University Dubai (HWUD), a position he has held since 2023. He serves as the Director of the Heriot-Watt Lab of AI and Robotics Research (LAIRR) and is the academic lead for the UAE Centre for Doctoral Training (CDT) in AI and Robotics, in collaboration with the Mohamed bin Zayed University of Artificial Intelligence (MBZUAI). Additionally, he is the Dubai representative for the Heriot-Watt Robotics and AI Global Research Institute (GRI).

Dr. Zito’s work has been supported by several prestigious funding bodies, including the EU's FP7 and H2020 initiatives, EPSRC, Airbus, the UK's National Centre of Nuclear Robotics (NCNR), and grants from Mubadala in Abu Dhabi. 

Before joining HWUD, Dr. Zito was a Lead Scientist in Robot Control and Learning at the Technology Innovation Institute (TII) in Abu Dhabi from 2020 to 2023. From 2015 to 2020, he held positions at the University of Birmingham, including Assistant Professor in AI and Computer Science and Senior Research Fellow at the Extreme Robotics Lab, Centre of Computational Neuroscience and Cognitive Robotics, and Intelligent Robotics Lab.

As a member of IEEE RAS, Dr. Zito has published over 20 research papers in peer-reviewed journals and international conferences, contributing significantly to the field of robotics and AI. He is an alumnus of the EU-Japan Centre 'Vulcanus in Japan' since 2007.

• Cognitive Robotics
• Robot Manipulation
• Robot Learning
• Autonomous Systems in Hazardous Environments
• Physical Human-Robot Interaction (pHRI)
• Artificial Intelligence (AI)
• Machine Learning (ML)
• Remote Teleoperation
• Wearable Robotics

• Director of the Lab of AI and Robotics Research (LAIRR)
• Academic lead for the UAE Centre for Doctoral Training (CDT) in AI and Robotics, in collaboration with Mohamed bin Zayed University of Artificial Intelligence (MBZUAI)
• Dubai representative for the Heriot-Watt Robotics and AI Global Research Institute (GRI)

Dr. Zito's research is conducted within the framework of the Heriot-Watt Lab of AI and Robotics Research (LAIRR), which is closely integrated with the HW Robotics and AI Global Research Institute (GRI). His work aligns with the UAE's objectives of economic diversification and the development of a knowledge-based economy. This research group focuses on creating innovative solutions that combine cognitive robotics, AI, and advanced modeling techniques to tackle real-world challenges, particularly those involving autonomous systems in hazardous environments.

Dr. Claudio Zito is an Assistant Professor at HWUD with extensive experience in research and technological development within cognitive robotics. He has over 10 years of experience managing high-tech projects, supported by leading EU and local grants. His research contributes to the development of autonomous systems capable of safer and more efficient operation in complex and hazardous scenarios.