The following schedule is a work in progress. Once we have the full seminar details including title, abstract and speaker bio, we will publish these talks under upcoming seminars.
| Date | Speaker | Title | Abstract |
|---|---|---|---|
| 2026-07-13 12:00 | Yuan Ma | Exploring Machine Learning Techniques for Automatic Story Generation | Recent advances in large language models (LLMs) have significantly improved the performance of Natural Language Processing (NLP) systems across a wide range of language generation tasks. Despite these achievements, generating coherent, creative, and meaningful long-form narratives remains a major challenge for artificial intelligence. Story generation requires not only linguistic fluency but also narrative planning, character consistency, contextual understanding, and the ability to convey human values and emotions. This research investigates automatic story generation for exploring machine creativity and improving the narrative capabilities of LLMs. |
| 2026-07-16 11:00 | |||
| 2026-07-20 11:00 | Dr. Nicky Zimmerman | Sign Language: Towards Sign Understanding for Robot Autonomy | Navigational signs are common aids for human wayfinding and scene understanding but are underutilized by robots. We argue that they benefit robot navigation and scene understanding, by directly encoding privileged information on actions, spatial regions, and relations. Interpreting signs in open-world settings remains a challenge owing to the complexity of scenes and signs, but recent advances in vision-language models (VLMs) make this feasible. To advance progress in this area, we introduce the task of visual sign grounding, which maps semantic instructions on signs to corresponding scene elements and navigational actions. We also outline different applications, such as localization and navigation, which benefit from the spatial-symbolic information encoded by navigational signs. |
| 2026-07-21 11:00 | Prof. Panpan Cai | Commonsense Robot Planning with LLMs and Tree Search | Robots operating in human environments must plan under uncertainty while reasoning with commonsense knowledge that is often implicit, incomplete, or only expressed through natural language. Recent Large Language Models (LLMs) provide powerful semantic priors for robot planning, but their open-ended reasoning is difficult to integrate with structured decision making, long-horizon planning, and uncertainty-aware execution. In this talk, I will present a series of works that bridge LLMs with symbolic, tree-search-based planning for commonsense robotic task planning. First, I will introduce Tru-POMDP, which combines LLM-generated structured hypotheses with principled POMDP planning, enabling robots to reason over ambiguous instructions, hidden object locations, and open-world uncertainty through belief-space tree search. Next, I will present UniDomain, a framework that learns reusable symbolic planning domains from large-scale robot demonstrations, allowing robots to generalize to unseen manipulation tasks by composing domain knowledge instead of relying on handcrafted planning models. Finally, I will introduce PO-PDDL, a symbolic representation and learning framework that extends PDDL to model partial observability and stochastic action outcomes, enabling reusable belief-space planning models to be learned directly from robot execution videos. Overall, the talk argues that the future of robot planning lies not in replacing classical planning with LLMs, but in tightly integrating foundation models, symbolic world models, and principled search algorithms. Such an integration provides a practical path toward robots that can reason with commonsense, adapt to uncertainty, and execute complex long-horizon tasks in open-world environments. |
| 2026-07-23 11:00 | |||
| 2026-07-30 11:00 | |||
| 2026-08-06 11:00 | Mengxuan Zhang | ||
| 2026-08-13 11:00 | |||
| 2026-08-20 11:00 | |||
| 2026-08-27 11:00 | |||
| 2026-09-03 11:00 | Prof. Giles Hirst | Preaching to the converted. Tailoring large language model dialogue to differentiate idea endorsement and system evaluation. | Large language models (LLMs) are not only a relevant persuasive tool for politics, conspiracy theories, and contested social issues, but also for 'big business'. We examine how LLMs monetize and persuade in interactions, impacting (a) perceptions and support of new ideas (message) and (b) attitudes toward the Artificial Intelligence (AI) system (messenger). We theorize and support a two-sided commercial transaction: an LLM “speaking to” a user's moral values lead users to evaluate both message and messenger more positively: they are more likely to endorse, use, and pay for a novel concept and they perceive the LLM understands them better. Morally congruent framing sells the object as well as enhances perceptions of the user-AI relationship. Crucially, for message recipients, this effect is most pronounced for those with more polarized political identities, addressing who is most malleable through AI influence. |
| 2026-09-10 11:00 | |||
| 2026-09-17 11:00 | |||
| 2026-09-24 11:00 | Dr. Diego Marcondes | Informed Machine Learning and Explainability for Binary Image Processing via Mathematical Morphology | The explainability of machine learning methods has recently become an issue in virtually all domains of application, and important research lines aiming to explain high-performance black boxes have been explored within the explainable AI initiative. Alternatively, this talk presents a different line of research focused on developing fully interpretable models that can evolve into high-performance methods. In particular, we will discuss how informed machine learning, characterised by the insertion of strong domain knowledge into the model design, can be leveraged to obtain interpretable methods. The main concepts will be discussed in the context of binary image processing and recent developments in mathematical morphology, in particular the discrete morphological neural networks. |
| 2026-10-01 11:00 | |||
| 2026-10-08 11:00 | |||
| 2026-10-15 11:00 | |||
| 2026-10-22 11:00 | |||
| 2026-10-29 11:00 | |||
| 2026-11-05 11:00 | |||
| 2026-11-12 11:00 | |||
| 2026-11-19 11:00 | |||
| 2026-11-26 11:00 | |||
| 2026-12-03 11:00 |