2016 |
John N Karigiannis, Costas S Tzafestas Model-free learning on robot kinematic chains using a nested multi-agent topology Journal Article Journal of Experimental and Theoretical Artificial Intelligence, 28 (6), pp. 913–954, 2016, ISSN: 13623079. @article{321, title = {Model-free learning on robot kinematic chains using a nested multi-agent topology}, author = {John N Karigiannis and Costas S Tzafestas}, doi = {10.1080/0952813X.2015.1042923}, issn = {13623079}, year = {2016}, date = {2016-01-01}, journal = {Journal of Experimental and Theoretical Artificial Intelligence}, volume = {28}, number = {6}, pages = {913--954}, abstract = {This paper proposes a model-free learning scheme for the developmental acquisition of robot kinematic control and dexterous manipulation skills. The approach is based on a nested-hierarchical multi-agent architecture that intuitively encapsulates the topology of robot kinematic chains, where the activity of each independent degree-of-freedom (DOF) is finally mapped onto a distinct agent. Each one of those agents progressively evolves a local kinematic control strategy in a game-theoretic sense, that is, based on a partial (local) view of the whole system topology, which is incrementally updated through a recursive communication process according to the nested-hierarchical topology. Learning is thus approached not through demonstration and training but through an autonomous self-exploration process. A fuzzy reinforcement learning scheme is employed within each agent to enable efficient exploration in a continuous state–action domain. This paper constitutes in fact a proof of concept, demonstrating that glo...}, keywords = {}, pubstate = {published}, tppubtype = {article} } This paper proposes a model-free learning scheme for the developmental acquisition of robot kinematic control and dexterous manipulation skills. The approach is based on a nested-hierarchical multi-agent architecture that intuitively encapsulates the topology of robot kinematic chains, where the activity of each independent degree-of-freedom (DOF) is finally mapped onto a distinct agent. Each one of those agents progressively evolves a local kinematic control strategy in a game-theoretic sense, that is, based on a partial (local) view of the whole system topology, which is incrementally updated through a recursive communication process according to the nested-hierarchical topology. Learning is thus approached not through demonstration and training but through an autonomous self-exploration process. A fuzzy reinforcement learning scheme is employed within each agent to enable efficient exploration in a continuous state–action domain. This paper constitutes in fact a proof of concept, demonstrating that glo... |
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