Deploying Reinforcement Learning in the Real World: A Case Study on Apptronik Apollo

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dc.contributor.authorWelch, Stephen Brianen
dc.contributor.committeechairStilwell, Daniel J.en
dc.contributor.committeememberWilliams, Ryan K.en
dc.contributor.committeememberLeonessa, Alexanderen
dc.contributor.departmentElectrical Engineeringen
dc.date.accessioned2025-06-17T08:00:56Zen
dc.date.available2025-06-17T08:00:56Zen
dc.date.issued2025-06-16en
dc.description.abstractgeneralDeep reinforcement learning (RL) has gained increasing popularity as an approach to achieving dynamic behaviors on legged robots. However, transferring RL behaviors from simulation to reality is a challenging process: imperfect sensors, simulation models, control architecture, and latency all present obstacles to successfully deploying such approaches in the real world. In this thesis, we present an end-to-end overview of our approach to bridging the sim-to-real gap, leveraging domain randomization and careful choices in control architecture in order to successfully deploy RL policies for teleoperation in simulation and on hardware.en
dc.description.degreeMaster of Scienceen
dc.format.mediumETDen
dc.identifier.othervt_gsexam:44175en
dc.identifier.urihttps://hdl.handle.net/10919/135528en
dc.language.isoenen
dc.publisherVirginia Techen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectreinforcement learningen
dc.subjectroboticsen
dc.subjecthumanoid roboticsen
dc.titleDeploying Reinforcement Learning in the Real World: A Case Study on Apptronik Apolloen
dc.typeThesisen
thesis.degree.disciplineElectrical Engineeringen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.levelmastersen
thesis.degree.nameMaster of Scienceen

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