Focus Period Lund 2025

Focus Period Lund University 2025

Robot Learning

November 3 – December 5, 2025

Robot learning focuses on developing algorithms and methods that enable robots to learn from interactions with their environment, simulated experiences, as well as humans. In robot learning, traditional machine-learning approaches such as supervised learning and reinforcement learning take on new dimensions, while aspects such as machine learning, data efficiency, reliability, generalization, transfer learning, and safety gain increased importance.

Robot learning is driven by the need for robots to function effectively in diverse environments, allowing them to adapt to new tasks and interact with various objects and dynamic agents without continuous reprogramming. The growing interest in robot learning stems from its broad potential. In industry, robot learning can enable flexible automation systems capable of handling variable tasks like assembly, packaging, and inspection. Socially, advances in robot learning could facilitate the deployment of general-purpose robots in human-centered environments, such as homes, healthcare facilities, and educational institutions, while enabling specialized robots, like surgical robots, to operate with even greater precision. However, these advancements bring important considerations. The deployment of robots and autonomous systems that can learn and adapt raises questions around worker displacement, privacy, and ethical machine behavior, which must be part of a larger conversation. As robots become increasingly integrated into our daily lives, understanding and advancing robot learning is not just an academic pursuit but a necessity for a sustainable and beneficial integration.

Robot learning is an interdisciplinary field, drawing on insights from machine learning, robotics, control theory, and neuroscience to develop adaptive and intelligent systems. Collaboration across these fields is vital for advancing robot learning. As we progress toward a future with more autonomous systems, the ELLIIT focus period initiative seeks to review state-of-the-art techniques, address current and future challenges, and forecast the societal and industrial impacts of robot learning.

Scientific committee

The scientific committee consists of internationally renowned researchers, active within the topic of the focus period. The committee members, in collaboration with the organizers, suggest speakers for the symposium, and visiting scholars for the focus periods. The majority of the members of the scientific committee also contribute to the event as speakers during the symposium.

Yiannis Aloimonos

Professor at University of Maryland (USA)

Sylvain Calinon

Senior Research Scientist at Idiap Research Institute EPFL (Switzerland)

Jens Kober

Associate Professor at Technical University of Delft (The Netherlands)

Jean-Jacques Slotine

Professor at Massachusetts Institute of Technology, MIT (USA)

Carme Torras

Research Professor at UPC Technical University of Catalunia (Spain)

Organizing committee

The organizing committee consists of researchers and administrators from the ELLIIT institutions. Their role is to appoint the scientific committee, select speakers and visiting scholars, plan the focus period activities, and serve as hosts during the event.