A Multiagent Reinforcement Learning approach for inverse kinematics of high dimensional manipulators with precision positioning
Flexible manipulators based on soft robotic technologies demonstrate compliance and dexterous maneuverability with virtually infinite degrees-of-freedom. Such systems have great potential in assistive and surgical fields where safe human-robot interaction is a prime concern. However, in order to enable practical application in these environments, intelligent control frameworks are required that can automate low-level sensorimotor skills to reach targets with high precision. We designed a novel motor learning algorithm based on cooperative Multi-Agent Reinforcement Learning that enables high-dimensional manipulators to exploit an abstracted state-space through a reward-guided mechanism to find solutions that have a guaranteed precision. We test our algorithm on a simulated planar 6-DOF with a discrete action-set and show that the all the points reached by the manipulator average an accuracy of 0.0056m (±0.002). The algorithm was found to be repeatable. We further validated our concept on the Baxter robotic arm to generate solutions up to 0.008m, exceptions being the joint angle accuracy and calibration of the robot.
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A Multiagent Reinforcement Learning approach for inverse kinematics of high dimensional manipulators with precision positioning
Semantic Scholar · Engineering · 2016
Abstract
Flexible manipulators based on soft robotic technologies demonstrate compliance and dexterous maneuverability with virtually infinite degrees-of-freedom. Such systems have great potential in assistive and surgical fields where safe human-robot interaction is a prime concern. However, in order to enable practical application in these environments, intelligent control frameworks are required that can automate low-level sensorimotor skills to reach targets with high precision. We designed a novel motor learning algorithm based on cooperative Multi-Agent Reinforcement Learning that enables high-dimensional manipulators to exploit an abstracted state-space through a reward-guided mechanism to find solutions that have a guaranteed precision. We test our algorithm on a simulated planar 6-DOF with a discrete action-set and show that the all the points reached by the manipulator average an accuracy of 0.0056m (±0.002). The algorithm was found to be repeatable. We further validated our concept on the Baxter robotic arm to generate solutions up to 0.008m, exceptions being the joint angle accuracy and calibration of the robot.