Learning Semantically Meaningful and Actionable Representations with Ashutosh Saxena

In this episode I’m joined by Ashutosh Saxena, a veteran of Andrew Ng’s Stanford Machine Learning Group, and co-founder and CEO of Caspar.ai.

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Ashutosh and I discuss his RoboBrain project, a computational system that creates semantically meaningful and actionable representations of the objects, actions and observations that a robot experiences in its environment, and allows these to be shared and queried by other robots to learn new actions. We also discuss his startup Caspar, which applies these principles to the challenge of creating smart homes.

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About Ashutosh

• Ashutosh on Linkedin

Mentioned in the Interview

• Caspar.ai
• Paper: RoboBrain
• RoboBrain Website
• Paper: Structural-RNN: Deep Learning on Spatio-Temporal Graphs
• ROS: Open Source Operating System for Robots
• Cornell RoboBarista
• Cornell TellMeDave
• Baxter
• Cornell Machine Learning Lab
• Stanford Machine Learning Group
• TWIML Talk #107 – Peering into the Home w/ Aerial’s Wifi Motion Analytics with Michel Allegue & Negar Ghourchian
• TWIML Presents: Series page
• TWIML Events Page
• TWIML Meetup
• TWIML Newsletter

“More On That Later” by Lee Rosevere licensed under CC By 4.0