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Curriculum is more influential than haptic feedback when learning object manipulation.

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Reinforcement learning with curriculum design enables robotic hands to learn dexterous manipulation. Surprisingly, haptic feedback is not essential for this complex task, challenging prior assumptions.

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Machine Learning

Background:

  • Dexterous manipulation is a key challenge for autonomous robots.
  • Learning to lift and rotate objects with intermittent finger contacts is particularly difficult.

Purpose of the Study:

  • To investigate the impact of curriculum design and haptic feedback on learning dexterous manipulation.
  • To compare the effectiveness of no-tactile versus 3D-force sensing.

Main Methods:

  • Model-free reinforcement learning was employed with a simulated three-finger robotic hand.
  • A novel curriculum-based learning rate scheduler was developed to accelerate convergence.

Main Results:

  • The choice of curriculum significantly influenced the learning progression for dexterous manipulation across various objects.
  • Robotic hand successfully learned manipulation tasks even without haptic information, demonstrating robustness.

Conclusions:

  • Curriculum learning is a robust approach for teaching robots dexterous manipulation.
  • Haptic information may not be necessary for complex manipulation tasks, challenging conventional wisdom.