Senior Researcher, Fondazione Bruno Kessler

Talk title: Embodied perception and reasoning in human-centric environments

Recent advances in vision-language models and multimodal large language models have transformed embodied AI, enabling agents to move beyond specialist systems toward generalist models that can interpret visual observations and natural-language instructions to generate executable actions. While these models have demonstrated impressive capabilities in controlled laboratory settings, they continue to struggle in the human-centric environments where embodied agents will ultimately need to operate.

Unlike curated benchmarks, human-centric environments are visually diverse, spatially complex, and constantly evolving. Homes, for example, are highly personalized spaces whose objects are not only functional but also reflect individual preferences through subtle visual characteristics. Their layouts reflect personal styles being tidy or messy, and change with everyday activities: a cup may be neatly stored in a cupboard, sitting beside a sofa while in use, or left among dirty dishes in the sink. Human communication is equally variable, with instructions that may be brief, ambiguous, or even erroneous depending on the context and the individual. Addressing this complexity and variability is a key step toward deploying embodied AI in everyday settings.

In this talk, I will present a series of our recent works that enable embodied agents to better understand complex scenes and natural-language instructions, with applications to language-guided navigation and object grasping. Together, these works illustrate how advances in multimodal perception and reasoning can bring embodied agents closer to robust operation in the real world. 

Senior Data Scientist, Maggiolo Group

Talk tile: From Personality to Societies: the Micro-Macro link

Since about 20 years, researchers have leveraged the field of personality computing to extract psychological traits from digital footprints. Specifically, algorithms for personality recognition from social media can map individual psychological profiles (a micro-level) at a massive scale. 

At the macro-level, the emerging interdisciplinary field of cliodynamics can model large-scale social dynamics evolving through time. By applying historical phase recognition algorithms to historical databases, we can identify macro-structural transitions derived from collective behaviors—such as periods of political instability, economic stagnation, or state collapse. 

Connecting these two data-driven fields allows scientists to computationally analyze what sociologists call the "Micro-Macro link" between individuals and societies: how millions of micro-level individual expressions aggregate over time to trigger macro-historical phases, and conversely, how shifting societal eras alter cultural norms, shaping the behavior of individuals.

In this talk we will see an overview of personality computing in the last 20 years, the task of Historical Phase Recognition, the data to perform it, and potential impacts of LLM usage on collective memory.

Professor, University di Torino

Talk title: TBD