Reinforcement Learning and Synergistic Control of the ACT Hand

  title={Reinforcement Learning and Synergistic Control of the ACT Hand},
  author={Eric Rombokas and Mark Malhotra and Evangelos A. Theodorou and Emo Todorov and Yoky Matsuoka},
  journal={IEEE/ASME Transactions on Mechatronics},
Tendon-driven systems are ubiquitous in biology and provide considerable advantages for robotic manipulators, but control of these systems is challenging because of the increase in dimensionality and intrinsic nonlinearities. Researchers in biological movement control have suggested that the brain may employ “muscle synergies” to make planning, control, and learning more tractable by expressing the tendon space in a lower dimensional virtual synergistic space. We employ synergies that respect… CONTINUE READING
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Iterative Path Integral Stochastic Optimal Control: Theory and Applications to Motor Control

  • E. Theodorou
  • Ph.D. dissertation,
  • 2011
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