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A neural network-based exploratory learning and motor planning system for co-robots.

Byron V Galbraith1, Frank H Guenther2, Massimiliano Versace3

  • 1Program in Cognitive and Neural Systems, Boston University Boston, MA, USA ; Center for Excellence in Learning in Education, Science, and Technology, Boston University Boston, MA, USA ; Neuromorphics Laboratory, Boston University Boston, MA, USA.

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Collaborative robots learn motor skills through "learning by doing." This adaptive neural network system enables robots to coordinate movement for tasks like grasping objects and navigating dynamic environments.

Keywords:
co-robotegocentric navigationembodied AIexploratory learningmotor planningneural network

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Machine Learning

Background:

  • Collaborative robots (co-robots) require adaptability for dynamic environments.
  • Exploratory learning, or "learning by doing," is key for co-robot motor planning.
  • Unsupervised learning builds internal models from real-time sensory input.

Purpose of the Study:

  • Present an adaptive neural network system for co-robot control.
  • Utilize exploratory learning for coordinated motor planning.
  • Enable robots to navigate, reach, and grasp objects autonomously.

Main Methods:

  • Developed an adaptive neural network-based control system.
  • Employed exploratory learning for unsupervised skill acquisition.
  • Validated the system on the 11-DOF RoPro Calliope mobile robot.

Main Results:

  • The Calliope robot learned hand-eye-body coordination via "motor babbling."
  • The system successfully related visual and proprioceptive information.
  • Autonomous selection of wheel and joint velocities enabled task performance.

Conclusions:

  • The adaptive neural network system effectively facilitates co-robot learning.
  • Exploratory learning enables robots to master complex motor tasks.
  • The system allows co-robots to autonomously adapt to and execute tasks in real-world scenarios.