Self-exploration of the stumpy robot with predictive information maximization

Georg Martius*, Luisa Jahn, Helmut Hauser, Verena V. Hafner

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

5 Citations (Scopus)


One of the long-term goals of artificial life research is to create autonomous, self-motivated, and intelligent animats. We study an intrinsic motivation system for behavioral self-exploration based on the maximization of the predictive information using the Stumpy robot, which is the first evaluation of the algorithm on a real robot. The control is organized in a closed-loop fashion with a reactive controller that is subject to fast synaptic dynamics. Even though the available sensors of the robot produce very noisy and peaky signals, the self-exploration algorithm was successful and various emerging behaviors were observed.

Original languageEnglish
Title of host publicationFrom Animals to Animats 13 - 13th International Conference on Simulation of Adaptive Behavior, SAB 2014, Proceedings
PublisherSpringer Verlag
Number of pages11
ISBN (Print)9783319088631
Publication statusPublished - 1 Jan 2014
Event13th International Conference on the Simulation of Adaptive Behavior, SAB 2014 - Castellon, Spain
Duration: 22 Jul 201425 Jul 2014

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8575 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference13th International Conference on the Simulation of Adaptive Behavior, SAB 2014


  • dynamical systems
  • information theory
  • intrinsic motivation
  • learning
  • robot control
  • Self-exploration


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