How can we teach machines to communicate with humans and learn from language instructions, just like we do? Due to the complexity of this research question, I embrace a multi-disciplinary research agenda based on the following macro themes:

Vision and Perception

I am fascinated by the concept of grounded cognition according to which conceptual representations are a result of fusing multiple sources of perceptual information. Specifically, I’m interested in agents that can learn perceptual representations that are effective in downstream tasks involving high-order reasoning skills such as situated dialogue and language-guided task completion for embodied agents.

Natural Language Processing

My research agenda aims at learning word representations that can truly uncover their meanings. More broadly, I’m very interested in learning language representations that are grounded in perceptual experience. Such representations can then be transferred to other tasks such as language-guided task completion as well as other downstream tasks requiring commonsense knowledge.

Machine Learning and AI (inc. multi-agent systems)

I’m interested in developing robots that can learn multimodal representations from interaction with the world and with other agents. To implement such agents, we require sophisticated learning algorithms that facilitate learning from several supervision signals. In this interactive learning paradigm, several learning techniques are essential such as reinforcement learning and continual learning.

Human-Robot Interaction

The result of the fundamental research that I conduct at the intersection between Perception, NLP and Machine Learning, is fundamental to developing robots that can develop a symbiotic relationship with humans. Particularly, I’m interested in pushing the boundaries of HRI by moving towards Human-Robot Collaboration, a field in which humans and robots communicate to achieve common ground and improve each other’s skills.

An up-to-date list of my publications can be found on Google Scholar.

Alessandro Suglia is an Assistant Professor at Heriot-Watt University (HWU) and co-lead of the "Generative AI for Robotics" theme at the National Robotarium. I am also a member of the ELLIS network and the academic liaison between HWU and the Alan Turing Institute.

Alessandro’s research focuses on designing artificial agents that learn language by leveraging sensory information derived from interacting with the world and with other agents. During his PhD, he was one of the main developers of Alana, the Heriot-Watt conversational AI which ranked 3rd in the Amazon Alexa Prize challenge in 2018. In his role as Assistant Professor at HWU, he led the HWU team “EMMA”, the only non-American university team which was one of the finalists of the Amazon Simbot Challenge—the first Amazon competition to push the boundaries of Embodied Conversational AI. Alongside several academic collaborations, he also completed research collaborations with Amazon Alexa AI, Meta AI, and the European Space Agency focused on developing innovative Multimodal Generative AI models for embodied and situated human-robot interaction tasks.

1. co-lead of the "Generative AI for Robotics" theme at the National Robotarium
2. Academic liaison between Heriot-Watt University and Alan Turing Institute
3. Supervisor for the Robotics and Autonomous Systems CDT
4. Supervisor for the Sensing, Processing, and AI for Defence and Security CDT