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dc.contributor.authorTan, Jie
dc.date.accessioned2021-09-11T00:42:41Z
dc.date.available2021-09-11T00:42:41Z
dc.date.issued2021-09-01
dc.identifier.urihttp://hdl.handle.net/1853/64941
dc.descriptionPresented online via Bluejeans Events on September 1, 2021 at 12:15 p.m.en_US
dc.descriptionJie Tan is currently the Tech Lead Manager of the Robot Locomotion and Safety teams at Google Brain Robotics. His research enables robots to automatically learn skills to accomplish complex tasks in the real world. Dr. Tan's research interests include AI safety, machine learning, robotics, power grids and simulation.en_US
dc.descriptionRuntime: 51:54 minutesen_US
dc.description.abstractDeep Reinforcement Learning (DRL) holds the promise of designing complex robotic controllers automatically. In this talk, I will discuss two different approaches to apply deep reinforcement learning to learn locomotion controllers for legged robots. The first approach is through sim-to-real transfer. Due to safety concerns and limited data, most of the training is conducted in simulation. However, controllers learned in simulation usually perform poorly on real robots. I will present a set of techniques to overcome this sim-to-real gap. The second approach is to directly learn in the real world. Due to the complexity and diversity of the real environments, building a simulation that can faithfully model the real world is not always feasible. Having the ability to learn on the fly and adapt quickly in real-world scenarios is crucial for large-scale deployment of robots. I will discuss the challenges of training legged robots in the real world and various ways to address these challenges.en_US
dc.format.extent51:54 minutes
dc.language.isoen_USen_US
dc.relation.ispartofseriesML@GT Seminar Series;
dc.subjectLocomotionen_US
dc.subjectReinforcement learningen_US
dc.subjectRoboticsen_US
dc.subjectSimulationen_US
dc.titleLearning Locomotion: From Simulation to Real Worlden_US
dc.typeLectureen_US
dc.typeVideoen_US
dc.contributor.corporatenameGeorgia Institute of Technology. Machine Learningen_US
dc.contributor.corporatenameGoogleen_US


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