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Self-Organized Behavior Generation for Musculoskeletal Robots.

Ralf Der1, Georg Martius2

  • 1Institute for Computer Science, University of Leipzig Leipzig, Germany.

Frontiers in Neurorobotics
|April 1, 2017
PubMed
Summary
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This study introduces a self-learning robot controller that discovers object properties through physical interaction, enabling unique movement patterns for each object. This neurocontroller advances robot adaptability and understanding of sensorimotor control.

Area of Science:

  • Robotics and Control Systems
  • Artificial Intelligence
  • Dynamical Systems Theory

Background:

  • Traditional robot control struggles with complex, unknown dynamics.
  • Self-organized control offers scalability, robustness, and resilience for such systems.
  • Recent advancements in extrinsic differential plasticity provide a novel learning mechanism.

Purpose of the Study:

  • To present a self-learning neurocontroller utilizing extrinsic differential plasticity.
  • To apply this controller to an anthropomorphic robot arm interacting with objects of unknown dynamics.
  • To investigate the emergence of object-specific sensorimotor patterns and hand-eye coordination.

Main Methods:

  • Implementation of a self-learning neurocontroller based on extrinsic differential plasticity.
Keywords:
anthropomimeticlearningmusculoskeletalrobot controlself-explorationself-organizationtendon-driven

Related Experiment Videos

  • Experimental setup with an anthropomorphic robot arm and various attached objects (pendulum, bottle, wheel, brush).
  • Integration of camera-based hand coordinate feedback for hand-eye coordination.

    • Main Results:

    • The robot autonomously learns distinct sensorimotor patterns for different objects (e.g., circular motion for pendulum, shaking for bottle).
    • The robot demonstrates object recognition through learned dynamical patterns, discovering object affordances.
    • Spontaneous self-organization of hand-eye coordination is observed when visual feedback is included.
    • Behaviors emerge from operating at the border of instability, utilizing limit cycle attractors and attractor morphing.

    Conclusions:

    • The self-learning neurocontroller effectively enables robots to adapt to and learn from unknown object dynamics.
    • This approach facilitates the discovery of object affordances and robust sensorimotor pattern generation.
    • The findings offer insights into human sensorimotor control and subjective feelings, with potential feedback for robotics research.