2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2017
DOI: 10.1109/iros.2017.8205972
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Adjustable interaction control using genetic algorithm for enhanced coupled dynamics in tool-part contact

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Cited by 6 publications
(3 citation statements)
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“…In addition, due to the large number of generations and populations within each generation, the number of physical implementations of Evolutionary Algorithms has thus far been limited [11]. This is especially pronounced when taking into account that ERL requires online learning, though it does allow a bootstrapping process to precede.…”
Section: Accelerated Deep Neuroevolutionmentioning
confidence: 99%
“…In addition, due to the large number of generations and populations within each generation, the number of physical implementations of Evolutionary Algorithms has thus far been limited [11]. This is especially pronounced when taking into account that ERL requires online learning, though it does allow a bootstrapping process to precede.…”
Section: Accelerated Deep Neuroevolutionmentioning
confidence: 99%
“…First, they do not use an explicit objective function, implying that neither the robot nor environment models are needed [13]. Second, evolutionary algorithms are less susceptible to local minima due to statistical population sampling [14].…”
Section: Introductionmentioning
confidence: 99%
“…Li [23] optimized an impedance controller along one degree-of-freedom (DOF) in a simulated environment without considering the transition from free motion to constrained motion. Lahr et al [13] optimized an admittance controller for industrial robots along one DOF in an experimental setup, optimizing in an experimental design taking into account several nonlinearities. Nadeau and Bonev [16] used an evolutionary algorithm to optimize the impedance controller during a human-machine task with multiple DOFs using an industrial robot.…”
Section: Introductionmentioning
confidence: 99%