Abstract:
We apply path integral reinforcement learning to a biomechanically accurate dynamics model of the index finger and then to the Anatomically Correct Testbed (ACT) robotic ...Show MoreMetadata
Abstract:
We apply path integral reinforcement learning to a biomechanically accurate dynamics model of the index finger and then to the Anatomically Correct Testbed (ACT) robotic hand.We illustrate the applicability of Policy Improvement with Path Integrals (PI2) to parameterized and non-parameterized control policies. This method is based on sampling variations in control, executing them in the real world, and minimizing a cost function on the resulting performance. Iteratively improving the control policy based on real-world performance requires no direct modeling of tendon network nonlinearities and contact transitions, allowing improved task performance.
Date of Conference: 14-18 May 2012
Date Added to IEEE Xplore: 28 June 2012
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