Program

Below you can find the program for IMOL 2022 at the Max Planck Institue for Intelligent Systems. We also provide a downloadable PDF version of the program.

All posters will be displayed during the whole workshop. The poster presentations are split into poster session A on the first day (odd numbers, green) and poster session B on the second day (even numbers, black).  Below and in the downloadable program you can find a list of all accepted posters.

Posters:

1. Aviv Tamar, Daniel Soudry and Ev Zisselman: Learning to Explore from Data — a Bayesian RL Perspective
2. Mehdi Zadem, Sergio Mover, Sao Mai Nguyen and Sylvie Putot: Towards Automata-Based Abstraction of Goals in Hierarchical Reinforcement Learning
3. Valentin Marcel and Matej Hoffmann: Learning self-reaching using a generative model from self-touch configurations 
4. Jason Khoury, Sergiu Tcaci Popescu and Matej Hoffmann:  Intrinsic motivation in infant’s self-touch exploration 
5. Filipe Gama, Maksym Shcherban, Matthias Rolf and Matej Hoffmann: Active tactile exploration for body model learning
6. Fedor Scholz, Christian Gumbsch, Sebastian Otte and Martin V. Butz: Inference of Affordances and Active Motor Control in Simulated Agents
7. Billy I. Lyons and J. Michael Herrmann: Learning to teach by reflexive reinforcement learning
8. Pierre Schumacher, Daniel Häufle, Dieter Büchler and Georg Martius: Show Me What You Can: Intrinsic Self-Exploration of Muscle-Driven Systems
9. Cansu Sancaktar, Arash Tavakoli and Georg Martius: Curious Exploration via Structured World Models
10. Alejandro Romero, Gianluca Baldassarre, Richard J. Duro and Vieri Giuliano Santucci: Autonomous learning of interdependent goals in non-stationary environments
11. Franziska Brändle, Lena Stocks, Joshua Tenenbaum, Samuel Gershman and Eric Schulz: Intrinsically Motivated Exploration as Empowerment
12. Marcel Binz and Eric Schulz: Exploration With a Finite Brain
13. Emilio Cartoni, Davide Montella, Jochen Triesch and Gianluca Baldassarre: Robot open-ended autonomous learning architectures: challenges and solutions in the REAL testbed
14. Thomas Schnürer, Malte Probst and Horst-Michael Gross: Utilizing Emergent, Task-Independent Knowledge Representations for Accelerated Task-Learning in Reinforcement Learning
15. Cédric Colas, Tristan Karch, Thomas Carta, Clément Moulin-Frier and Pierre-Yves Oudeyer: Towards a Vygotskian Autotelic Artificial Intelligence: The Internalization of Cognitive Tools from Rich Socio-Cultural Worlds
16. Ahmed Akakzia, Olivier Serris, Olivier Sigaud and Cédric Colas: Help Me Explore: Minimal Social Interventions for Autotelic Agents
17. Louis Annabi: Intrinsically motivated learning of causal world models