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dc.contributor.authorSadanandan Anand, Akhil
dc.contributor.authorZhao, Guoping
dc.contributor.authorRoth, Hubert
dc.contributor.authorSeyfarth, Andre
dc.date.accessioned2022-02-15T09:12:09Z
dc.date.available2022-02-15T09:12:09Z
dc.date.created2022-02-14T09:02:14Z
dc.date.issued2019
dc.identifier.issn2164-0572
dc.identifier.urihttps://hdl.handle.net/11250/2978986
dc.description.abstractA gait model capable of generating human-like walking behavior at both the kinematic and the muscular level can be a very useful framework for developing control schemes for humanoids and wearable robots such as exoskeletons and prostheses. In this work we demonstrated the feasibility of using deep reinforcement learning based approach for neuromuscular gait modelling. A lower limb gait model consists of seven segments, fourteen degrees of freedom, and twenty two Hill-type muscles was built to capture human leg dynamics and the characteristics of muscle properties. We implemented the proximal policy optimization algorithm to learn the sensory-motor mappings (control policy) and generate human-like walking behavior for the model. Human motion capture data, muscle activation patterns and metabolic cost estimation were included in the reward function for training. The results show that the model can closely reproduce the human kinematics and ground reaction forces during walking. It is capable of generating human walking behavior in a speed range from 0.6 m/s to 1.2 m/s. It is also able to withstand unexpected hip torque perturbations during walking. We further explored the advantages of using the neuromuscular based model over the ideal joint torque based model. We observed that the neuromuscular model is more sample efficient compared to the torque model.en_US
dc.language.isoengen_US
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_US
dc.titleA deep reinforcement learning based approach towards generating human walking behavior with a neuromuscular modelen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.description.versionacceptedVersionen_US
dc.rights.holder© IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.en_US
dc.source.journalIEEE-RAS International Conference on Humanoid Robotsen_US
dc.identifier.doi10.1109/Humanoids43949.2019.9035034
dc.identifier.cristin2001141
cristin.ispublishedtrue
cristin.fulltextpostprint
cristin.qualitycode1


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