Title:
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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