Title:
The Right Stuff: Representing Safety to Get Robots Out in the Real World
The Right Stuff: Representing Safety to Get Robots Out in the Real World
dc.contributor.author | Kousik, Shreyas | |
dc.contributor.corporatename | Georgia Institute of Technology. Institute for Robotics and Intelligent Machines | en_US |
dc.contributor.corporatename | Georgia Institute of Technology. School of Mechanical Engineering | en_US |
dc.date.accessioned | 2023-02-22T16:07:13Z | |
dc.date.available | 2023-02-22T16:07:13Z | |
dc.date.issued | 2023-02-08 | |
dc.description | Presented on February 8, 2023 at 12:15 p.m. in the Marcus Nanotechnology Building, Room 1116. | en_US |
dc.description | Shreyas Kousik is an assistant professor at Georgia Tech in the George W. Woodruff School of Mechanical Engineering. He studies how to make autonomous systems, such as robots, safe and practical. | en_US |
dc.description | Runtime: 60:14 minutes | en_US |
dc.description.abstract | Autonomous robots have the incredible potential to aid people by taking on difficult tasks and working alongside us. However, it will be difficult to trust robots in widespread deployment without knowing when they are safe. Safety can often be expressed theoretically yet suffer an imperfect translation into numerical representation. My research focuses on this gap: what are the right representations of robot safety to bridge theory and real-world deployment? For this talk, I focus on safety in collision avoidance for robot motion planning. In particular, I present Reachability-based Trajectory Design (RTD), a framework that unites theory and representation for real-time, safe robot motion planning. RTD’s foundation in theory makes it applicable to a wide variety of systems, including self-driving cars, quadrotor drones, and manipulator arms. In practice, over thousands of simulations and dozens of hardware trials, RTD has resulted in no collisions while outperforming other methods, establishing a new state of the art. My future work extends from this paradigm to enable robots to learn and adapt their own notions of safety in three ways: online adaptive dynamic model identification for safe motion planning, robust perception that is targeted towards safe control, and co-design of a robot’s perception, planning, and control algorithms to reduce overly cautious robot behavior without losing safety guarantees. In each of these future directions I seek to create and deploy the right representations to transfer theory onto hardware, to make robots do more amazing things safely. | en_US |
dc.format.extent | 60:14 minutes | |
dc.identifier.uri | http://hdl.handle.net/1853/70291 | |
dc.language.iso | en_US | en_US |
dc.publisher | Georgia Institute of Technology | en_US |
dc.subject | Learning | en_US |
dc.subject | Optimization | en_US |
dc.subject | Safety | en_US |
dc.title | The Right Stuff: Representing Safety to Get Robots Out in the Real World | en_US |
dc.type | Moving Image | en_US |
dc.type.genre | Lecture | |
dspace.entity.type | Publication | |
local.contributor.corporatename | Institute for Robotics and Intelligent Machines (IRIM) | |
local.relation.ispartofseries | IRIM Seminar Series | |
relation.isOrgUnitOfPublication | 66259949-abfd-45c2-9dcc-5a6f2c013bcf | |
relation.isSeriesOfPublication | 9bcc24f0-cb07-4df8-9acb-94b7b80c1e46 |
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