Hybrid OSC-RL Control for Task Optimization of Dual-Arm Robots

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dc.catalogadorgrr
dc.contributor.authorGalarce Acevedo, Patricio
dc.contributor.authorTorres Torriti, Miguel Attilio
dc.date.accessioned2024-08-21T16:09:31Z
dc.date.available2024-08-21T16:09:31Z
dc.date.issued2024
dc.description.abstractIn this work we present a strategy to solve the task optimization problem for dual-arm mobile manipulators in the context of agricultural tasks. The strategy combines a Reinforcement Learning (RL) agent with a low-level Operational Space Controller (OSC). The agent is responsible for motion planning, as well as compensatory torque computation. Preliminary results obtained through physically accurate simulation using MuJoCo show that the method proposed achieves a higher task success rate in task completion.
dc.fuente.origenORCID
dc.identifier.doi10.1109/romoco60539.2024.10604418
dc.identifier.scopusidSCOPUS_ID:2-s2.0-85201179356
dc.identifier.urihttp://dx.doi.org/10.1109/romoco60539.2024.10604418
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/87577
dc.information.autorucEscuela de Ingeniería; Galarce Acevedo, Patricio; S/I; 201334
dc.information.autorucEscuela de Ingeniería; Torres Torriti, Miguel Attilio; 0000-0002-7904-7981; 96590
dc.language.isoen
dc.nota.accesocontenido parcial
dc.pagina.final222
dc.pagina.inicio217
dc.relation.ispartofProceedings of the 13th International Workshop on Robot Motion and Control
dc.revistaIEEE Xplore
dc.rightsacceso restringido
dc.subjectTraining
dc.subjectRobot motion
dc.subjectTorque
dc.subjectReinforcement learning
dc.subjectEnd effectors
dc.subjectPlanning
dc.subjectTask analysis
dc.subject.ddc600
dc.subject.deweyTecnologíaes_ES
dc.titleHybrid OSC-RL Control for Task Optimization of Dual-Arm Robots
dc.typecomunicación de congreso
sipa.codpersvinculados201334
sipa.codpersvinculados96590
sipa.trazabilidadORCID;2024-08-19
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