From Seeing to Doing: Understanding & Interacting With The Real World With Fei-Fei Li
Conference: Transform X 2021
2021-10-07
Authors: Fei-Fei Li
Summary
The presentation discusses the creation of a large-scale and diverse robotic learning simulation environment and benchmark called Behavior Benchmark for everyday household activities in virtual interactive ecological environments.
- The goal is to create a robotic learning simulation environment and benchmark that mimics the real world as much as possible
- The environment is large-scale and diverse, with a thousand activities or tasks, 50 large-scale real-world things, 8 scene types, more than 2000 object categories, 3000 object models, and a large number of objects per activity
- The tasks are complex, like long horizon and multi-steps, and have standardized and flexible evaluation metrics
- The approach is human-centered, taking all the potential tasks that people do through surveys like American Bureau of Labor Statistics and Eurostats
- The simulation environment has photorealistic rendering, kinematic and dynamic extended states, flexible materials, deformable bodies, realistic fluids, thermal effects, realistic action executions, and object distributions
Abstract
Dr. Fei-Fei-Li, Sequoia Professor of Computer Science at Stanford University and Denning Co-Director of the Stanford Institute for Human-Centered AI (HAI) explores the evolutionary origins of vision and how it is the 'cornerstone for intelligence', for both humans and machines alike. Dr. Li shares how vision is critical for first perceiving the physical world, and then interacting with it. She highlights recent advances in AI research which helps machines perceive the environment around them and then engage with it, to perform both short- and long-horizon tasks. See how BEHAVIOR, a benchmark of everyday activities, can help robots learn to perform increasingly complex tasks, by composing smaller actions to achieve more elaborate goals, for example, clearing up a table or putting away toys.
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