Abstract

[email protected]’s mission is to make robots useful in the real world through machine learning. We are excited about a new model for robotics, designed for generalization across diverse environments and instructions. This model is focused on scalable data-driven learning, which is task-agnostic, leverages simulation, learns from past experience, and can be quickly adapted to work in the real-world through limited interactions. In this talk, we’ll share some of our recent work in this direction in both manipulation and locomotion applications.

Biography

Carolina Parada is a Senior Engineering Manager at Google Robotics. She leads the robot-mobility group, which focuses on improving robot motion planning, navigation, and locomotion, using reinforcement learning. Prior to that, she led the camera perception team for self-driving cars at Nvidia for 2 years. She was also a lead with Speech @ Google for 7 years, where she drove multiple research and engineering efforts that enabled Ok Google, the Google Assistant, and Voice-Search. Carolina grew up in Venezuela and moved to the US to pursue a B.S. and M.S. degree in Electrical Engineering at University of Washington and her Phd at Johns Hopkins University at the Center for Language and Speech Processing (CLSP).