A robot that can read a room—and respond accordingly—is the goal of a new European training network for doctoral researchers. Most service robots today follow rigid scripts. They cannot tell when a person is annoyed, confused, or culturally uncomfortable, which limits their use in hospitals, schools, and public spaces. SWEET addresses this gap by training a generation of researchers who can build robots that perceive human emotions, intentions, and cultural signals in real time, then adapt their behaviour. If the project succeeds, it will produce robots that do not simply execute commands but navigate social situations—for instance, a hospital delivery robot that waits politely rather than interrupting a conversation, or a museum guide that adjusts its explanations for different audiences. The network brings together computer scientists, psychologists, and industry partners to ensure the robots are technically robust, ethically sound, and economically viable. This is primarily a training programme, not a single technology demonstrator. Its lasting impact will be the people it produces: doctoral graduates who understand both the engineering and the social dimensions of robotics, and who can carry that expertise into industry or academia.
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To develop autonomous robots that are able to comply with social conventions and expectations, and avoid rejection from humans requires that robots must be aware of the social context in which they operate. To this extent, robots need to be endowed with high levels of reactivity, proactivity, responsiveness, and intelligibility. The Doctoral Network -Industrial Doctorates on Social aWareness for sErvicE roboTs (SWEET) aims at training a new generation of research and professional figures able to advance the development of socially aware robots capable of perceiving, interpreting, and responding to human emotions, intentions, and cultural differences. The training program will offer a diverse curriculum, encompassing theoretical knowledge, hands-on technical skills, and real-world application scenarios. The network's interdisciplinary approach includes various fields, such as artificial intelligence, machine learning, human-robot interaction, computer vision, and cognitive sciences. Doctoral candidates will be immersed in cutting-edge research and innovation, gaining insights from the experience of both industrial and academic acclaimed research groups. The network will place a strong emphasis on ethical considerations and responsible innovation, deploying socially aware robots aligned with societal values and promoting inclusivity. Doctoral Candidates will also have the opportunity to participate in a unique coaching program for continuous professional development of their soft and leadership individual skills. Integration Milestones following a Scenario-Based Learning approach will provide co-working activities where collaborative design/implementation is fostered. The inter-sectoral collaboration between academia, user groups' representatives, business developers, and robot manufacturers of the project will further strengthen the novelty and impact of the research and training, and that research results are economically, socially, and technically feasible.
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