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Goal Oriented Behavior With a Habit-Based Adaptive Sensorimotor Map Network.

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  • 1Artificial Life and Minds Lab, School of Computer Science, University of Auckland, Auckland, New Zealand.

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Summary
This summary is machine-generated.

We introduce an adaptive sensorimotor (ASM) network, a novel robot controller based on habits and enactive cognition. This model demonstrates learning in robots without explicit representations, adapting behavior internally.

Keywords:
adaptive autonomyenactivismhabitminimal cognitionrobot controllersensorimotor contingencies

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Area of Science:

  • Robotics
  • Cognitive Science
  • Artificial Intelligence

Background:

  • Enactive cognition theories emphasize habit and agency at the sensorimotor level.
  • Existing models often rely on explicit representations or external fitness variables for learning.

Purpose of the Study:

  • To introduce a new habit-based robot controller model: the adaptive sensorimotor (ASM) network.
  • To provide a platform for investigating the link between networked habits and cognitive behavior.
  • To demonstrate robot learning using enactive principles without explicit representations.

Main Methods:

  • Developed an ASM-network combining continuous motor activity generation from historical sensorimotor trajectories.
  • Integrated an evaluative mechanism to reinforce or weaken trajectories based on supporting higher-order sensorimotor coordinations.
  • Applied the model to a minimal cognition task of object discrimination using a single robot.

Main Results:

  • The robot learned the object discrimination task through exploration and repetition of supportive trajectories.
  • Behavior adapted based on the internal requirements of the action-generating mechanism, not external variables.
  • Demonstrated recognizable learning behavior without explicit representational mechanisms or extraneous fitness variables.

Conclusions:

  • The ASM-network effectively models habit-based control and learning in robots.
  • Enactive principles can enable robots to learn and adapt through internal dynamics.
  • This approach offers a pathway for developing more autonomous and cognitively inspired robots.