Title:
Learning Locomotion: From Simulation to Real World
Learning Locomotion: From Simulation to Real World
| dc.contributor.author | Tan, Jie | |
| dc.contributor.corporatename | Georgia Institute of Technology. Machine Learning | en_US |
| dc.contributor.corporatename | en_US | |
| dc.date.accessioned | 2021-09-11T00:42:41Z | |
| dc.date.available | 2021-09-11T00:42:41Z | |
| dc.date.issued | 2021-09-01 | |
| dc.description | Presented online via Bluejeans Events on September 1, 2021 at 12:15 p.m. | en_US |
| dc.description | Jie 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.description | Runtime: 51:54 minutes | en_US |
| dc.description.abstract | Deep 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.extent | 51:54 minutes | |
| dc.identifier.uri | http://hdl.handle.net/1853/64941 | |
| dc.language.iso | en_US | en_US |
| dc.relation.ispartofseries | Machine Learning @ Georgia Tech (ML@GT) Seminar Series | |
| dc.subject | Locomotion | en_US |
| dc.subject | Reinforcement learning | en_US |
| dc.subject | Robotics | en_US |
| dc.subject | Simulation | en_US |
| dc.title | Learning Locomotion: From Simulation to Real World | en_US |
| dc.type | Moving Image | |
| dc.type.genre | Lecture | |
| dspace.entity.type | Publication | |
| local.contributor.corporatename | Machine Learning Center | |
| local.contributor.corporatename | College of Computing | |
| local.relation.ispartofseries | ML@GT Seminar Series | |
| relation.isOrgUnitOfPublication | 46450b94-7ae8-4849-a910-5ae38611c691 | |
| relation.isOrgUnitOfPublication | c8892b3c-8db6-4b7b-a33a-1b67f7db2021 | |
| relation.isSeriesOfPublication | 9fb2e77c-08ff-46d7-b903-747cf7406244 |
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