The Fifth International Workshop on

Intrinsically Motivated Open-ended Learning

IMOL 2022

4th-6th of April 2022

Max Planck Institute for Intelligent Systems,

Tübingen, Germany

IMOL 2022 is the fifth international workshop on Intrinsically Motivated Open-ended Learning. Following previous editions, IMOL 2022 aims to further explore the promise of intrinsically motivated open-ended lifelong learning in robots and artificial systems.

Aims of the workshop:

Following the four previous workshops, IMOL 2022 will explore the advancements of intrinsically motivated open-ended lifelong learning. One of our goals is to bring together researchers coming from different fields related to open-ended learning and autonomous development. The workshop aims to be a highly interactive event with high-profile keynote presentations and the participation of an audience. We hope to foster close interactions among the participants with discussions, poster sessions, and collective round tables.

Important dates:

Abstract submission: Feb 4th 2022

Conference dates: April 4-6th 2022

CONFIRMED SPEAKERS

Biography

My research focuses on how children and adults actively search for information when making decisions, drawing causal inferences and solving categorization tasks. Search strategies, as any other kind of strategies, are not always effective, because their usefulness and performance depends on the characteristics of the problem presented. In this sense, I am interested in how adaptive children and adults’ search for information strategies are, how sensitive and responsive they are to the structure of the tasks. I am especially interested in how actively searching for information, being able to generate the information we are interested in and to focus on what we consider most relevant, can impact our learning, understanding and explanations.

Talk

The emergence and developmental trajectory of active and ecological learning

This talk will introduce the Ecological Learning framework, which focuses on children’s ability to adapt and tailor their active learning strategies to the particular structure and characteristics of a learning environment. In particular, I will present the results of several seminal studies indicating that efficient, adaptive search strategies emerge around 3 years of age, much earlier than previously assumed. This work highlights the importance of developing age-appropriate paradigms that capture children’s early competence to gain a more comprehensive and fair picture of their active learning abilities. Also, it offers a process-oriented theoretical framework that can accommodate and reconcile a sparse but growing body of work documenting children’s active and adaptive learning.

Biography

Daniel Polani is Professor of Artificial Intelligence, Director of the Centre for Computer Science and Informatics Research (CCSIR) and Head of the Adaptive Systems Research Group at the University of Hertfordshire. His research interests principles of cognition and intelligent decision-making, expressed in the language of information theory. In the past decades, he developed a fully information-theoretic account of the perception-action loop. Amongst other, he developed the concept of relevant information which permit an information-theoretic approach to decision-making under bounded informational resources to understand cognitive constraints to task execution. He pioneered the concept of empowerment, the external action-perception channel capacity of an agent, which has been shown to be a general and widely applicable model for generic taskless behaviour generation in ill-specified domains. This including assistance of third parties. The empowerment model is now also used by companies such as DeepMind, Volkswagen and others. The information-theoretic framework has been also more widely used to model multi-agent scenarios. He has been PI e.g. in the Horizon 2020 projects socSMCs (about social sensorimotor contingencies) and WiMUST (mission control of robotic submarine UAVs), FP7 CORBYS (cognitive control framework for robotic systems), and Co-PI in FP7 RoboSkin (artificial skin technology and its detection) and FEELIX GROWING (robots detecting and responding to emotional cue). He was President of the RoboCup Federation in 2017-2019.

Biography

Deepak Pathak is a faculty in the School of Computer Science at Carnegie Mellon University. He received his Ph.D. from UC Berkeley and his research spans computer vision, machine learning, and robotics. He is a recipient of the faculty awards from Google, Sony, GoodAI, Samsung, and graduate fellowship awards from Facebook, NVIDIA, Snapchat. His research has been featured in popular press outlets, including The Economist, The Wall Street Journal, Quanta Magazine, Washington Post, CNET, Wired, and MIT Technology Review among others. Deepak received his Bachelor’s from IIT Kanpur with a Gold Medal in Computer Science. He co-founded VisageMap Inc. later acquired by FaceFirst Inc. For details: https://www.cs.cmu.edu/~dpathak/

Biography

Jochen Triesch received his Diploma and Ph.D. degrees in Physics from the University of Bochum, Germany, in 1994 and 1999, respectively. After two years as a post-doctoral fellow at the Computer Science Department of the University of Rochester, NY, USA, he joined the faculty of the Cognitive Science Department at UC San Diego, USA as an Assistant Professor in 2001. In 2005 he became a Fellow of the Frankfurt Institute for Advanced Studies (FIAS), in Frankfurt am Main, Germany. In 2006 he received a Marie Curie Excellence Center Award of the European Union. Since 2007 he is the Johanna Quandt Research Professor for Theoretical Life Sciences at FIAS. He also holds professorships at the Department of Physics and the Department of Computer Science and Mathematics at the Goethe University in Frankfurt am Main, Germany. In 2019 he obtained a visiting professorship at the Université Clermont Auvergne, France. His research interests span Computational Neuroscience, Machine Learning, and Developmental AI.

Talk

Learning to see — autonomously

Biography

Jun Tani received the D.Eng. degree from Sophia University, Tokyo in 1995. He started his research career with Sony Computer Science Lab. in 1993. He became a Team Leader of the Laboratory for Behavior and Dynamic Cognition, RIKEN Brain Science Institute, Saitama, Japan in 2001. He became a Full Professor with the Electrical Engineering Department, Korea Advanced Institute of Science and Technology, Daejeon, South Korea in 2012. He is currently a Full Professor with the Okinawa Institute of Science and Technology, Okinawa, Japan. He is also a visiting professor of The Technical University of Munich. His current research interests include cognitive neuroscience, developmental psychology, phenomenology, complex adaptive systems, and robotics. He is an author of “Exploring Robotic Minds: Actions, Symbols, and Consciousness as Self-Organizing Dynamic Phenomena.” published from Oxford Univ. Press in 2016.

Talk

Neurorobotics experiments on goal-directed planning based on active inference

Biography

Prof. Dr. Martin V. Butz has been a professor at the Department of Computer Science and the Department of Psychology, Faculty of Science, University of Tübingen, Germany, since 2011. His main background lies in computer science and machine learning (Diploma from Würzburg University and PhD from the University of Illinois at Urbana-Champaign, IL, USA; both in Computer Science). Throughout his research career he has been collaborating with researchers from various other disciplines, including cognitive and developmental psychologists, computational neuroscientists, roboticists, and linguists. His research focuses on neuro-computational, cognitive modeling and cognitive science more generally. A current main focus lies in uncovering conceptual, compositional, causal structures from sensorimotor experiences in humans and artificial systems. Important recent publications include his co-authored monograph: “How the Mind Comes Into Being: Introducing Cognitive Science from a Functional and Computational Perspective” (Oxford University Press, 2017), a special issue on “Event-Predictive Cognition” (2021, Topics in Cognitive Science), and a computational perspective on human and artificial intelligence (“Towards Strong AI”, 2021, Künstliche  Intelligenz).

Talk

Event-Predictive Active Inference

Humans develop event-predictive, generative, compositional structures from their actively gathered sensorimotor-based experiences. These grounded structures seem to be critical for developing higher-level cognitive abilities, including analogical, counterfactual, social reasoning, and learning a language. Meanwhile, the structures tend to uncover the hidden processes and causes that lie behind the sensorimotor experiences we actively gather. Motivated by this computational view, I will present recent insights gained on how similar structures may be learned by artificial systems. I will show several computational Bayesian and neural network models, which exhibit human-like cognitive and behavioral abilities and solve challenging planning and control problems. I conclude with some implications for future REAL systems, including both their potential abilities and necessary design considerations.

Biography

Martin Riedmiller is a research scientist and team-lead at DeepMind, London. Before joining DeepMind fulltime in spring 2015, he held several professor positions in machine learning and neuro-informatics from 2002 to 2015 at Dortmund, Osnabrück and Freiburg University. From 1998 to 2009 he lead the robot soccer team ‘Brainstormers’ that participated in the internationally renowned RoboCup competitions. As an early proof of the power of neural reinforcement learning techniques, the Brainstormers won the world championships for five times in both simulation and real robot leagues. He has contributed over 20 years in the fields of reinforcement learning, neural networks and learning control systems. He is author and co-author of some early and ground-lying work on efficient and robust supervised learning and reinforcement learning algorithms, including work on one of the first deep reinforcement learning systems.

Biography

Michael Spranger is a Senior Researcher at Sony Computer Science Laboratories and the COO of Sony AI. Michael’s research interest is to build creative, intelligent systems that learn autonomously in the real world. His work has spanned sensorimotor learning, the emergence of communication and foundations of AI.

Biography

Richard J. Duro is a Full Professor of Computer Science and Artificial Intelligence at the University of Coruña in Spain and the Coordinator of the Integrated Group for Engineering Research at this university since 1999. He received a PhD. in Physics from the University of Santiago de Compostela, Spain, in 1992, with work related to novel instrumentation systems in a collaboration with San Diego State University and the University of California San Diego where he did post-doctoral work. His teaching and research interests are in intelligent systems and autonomous robotics and his current work concentrates on motivational systems and developmental cognitive robotic architectures. Dr. Duro has published more than 250 articles in refereed journals, edited books and refereed conference proceedings, and co-authored 8 books. He is also the holder of 6 patents and 35 software registers. He is a Senior member of the IEEE and is currently a member of the Board of Governors of the International Neural Network Society (INNS) and its Vicepresident for Conferences and Technical Activities. He has been the Principal Investigator of more than 20 publicly funded research projects and over 100 contracts with industry.

Talk

Epistemic-MDB: First Steps Towards Purposeful IMOL

Biography

Sao Mai Nguyen specializes in cognitive developmental learning, reinforcement learning, imitation learning, automatic curriculum learning for robots : she develops algorithms for robots to learn multi-task controls by designing themselves their curriculum and by active imitation learning. She received her PhD from Inria in 2013 in computer science, for her machine learning algorithms combining reinforcement learning and active imitation learning for interactive and multi-task learning. She holds an Engineer degree from Ecole Polytechnique, France and a master’s degree in adaptive machine systems from Osaka University, Japan. She has coordinated project KERAAL to enable a robot to coach physical rehabilitation. She has participated in project AMUSAAL, for analysing human activities of daily living, and CPER VITAAL for developing assistive technologies for the elderly and disabled. She is currently an assistant professor at Ensta IP Paris, France and was previously with IMT Atlantique. She also acts as an associate editor of the journal IEEE TCDS and the co-chair of the Task force “Action and Perception” of the IEEE Technical Committee on Cognitive and Developmental Systems. For more information visit her webpage: https://nguyensmai.free.fr.

ORGANIZERS

Georg Martius

Organizer

Gianluca Baldassarre

Organizer

Vieri Giuliano Santucci

Organizer

Rania Rayyes

Organizer

Christian Gumbsch

Organizer

REAL 2021 COMPETITION

 

IMOL2022 will also host the REAL 2021 competition with a hands-on micro-workshop on how to participate. The “Robot open-Ended Autonomous Learning” (REAL) competition is focused on systems that acquire sensorimotor competence that allows them to interact with their physical environments in a full autonomous way.

The REAL 2021 competition is currently open for submissions. For more information on how to participate visit the competition website.

CONTACT & INFO

imol.conf@gmail.com

This initiative has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under Grant Agreement no 713110, project `GOAL-Robots — Goal-Based Open-Ended Autonomous Learning Robots’.

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