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Updated: May 4, 2026

MPI CyberMotion Simulator: Implementation of a Novel Motion Simulator to Investigate Multisensory Path Integration in Three Dimensions
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Curiosity driven reinforcement learning for motion planning on humanoids.

Mikhail Frank1, Jürgen Leitner1, Marijn Stollenga1

  • 1Dalle Molle Institute for Artificial Intelligence Lugano, Switzerland ; Facoltà di Scienze Informatiche, Università della Svizzera Italiana Lugano, Switzerland ; Dipartimento Tecnologie Innovative, Scuola Universitaria Professionale della Svizzera Italiana Manno, Switzerland.

Frontiers in Neurorobotics
|January 17, 2014
PubMed
Summary
This summary is machine-generated.

This study introduces an embodied artificial curiosity (AC) agent on the iCub humanoid robot, demonstrating intelligent real-time exploration and learning in complex environments.

Keywords:
artificial curiosityembodied AIhumanoidiCubintrinsic motivationreinforcement learning

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Reinforcement Learning

Background:

  • Previous artificial curiosity (AC) research is largely theoretical or limited to simple simulations.
  • Embodied agents in complex, real-world scenarios are under-explored in AC research.

Purpose of the Study:

  • To develop and evaluate an embodied artificial curiosity agent for real-time motion planning on a humanoid robot.
  • To investigate intelligent exploration strategies in a complex physical system.

Main Methods:

  • Implemented a novel reinforcement learning (RL) framework with a low-level reactive control layer for the iCub humanoid robot.
  • Developed a high-level curious agent utilizing information gain maximization and real-time world model learning.
  • Integrated the curious agent with the iCub's hardware for direct control and exploration.

Main Results:

  • The embodied agent successfully learned compact Markov models of the iCub's configuration space.
  • The iCub demonstrated intelligent exploration, focusing on physical constraints and environmental objects.
  • Achieved real-time motion planning and control on actual humanoid hardware.

Conclusions:

  • This work presents the first embodied curious agent for real-time humanoid motion planning.
  • The framework enables intelligent exploration and learning in complex robotic systems.
  • Demonstrates the potential of artificial curiosity for advanced robotic interaction and adaptation.