Posted by Michael S. Ryoo, Research Scientist and AJ Piergiovanni, Student Researcher, Robotics at Google Video understanding is a challenging problem. Because a video contains spatio-temporal data, its feature representation is required to abstract both appearance and motion information.Read Full Story
We hosted the first OpenAI Robotics Symposium on April 27, 2019. Robots that learn are an exciting path forward, yet there are differing approaches and opinions on how to make progress.Read Full Story
We are launching an AI lab. The goal is to learn unsupervised learning through Robotics (Cobots)
Long seen as a poor cousin to supervised learning – with Variational autoencoders, Reinforcement learning and Generative-Adversarial networks , unsupervised learning techniques have moved beyond the limitations of autoencoders.
From Oct 2018 to March 2019 , we are running a pilot.
One of the most important fields in robotics is the optimization of controllers. Currently, robots are often treated as a black box in this optimization process, which is the reason why derivative-free optimization methods such as evolutionary algorithms or reinforcement learning are omnipresent.
Posted by Kevin Zakka, Research Intern and Andy Zeng, Research Scientist, Robotics at Google Our physical world is full of different shapes, and learning how they are all interconnected is a natural part of interacting with our surroundings — for example, we understand that coat hangers hook onto clothing racks, power plugs insert into wall outlets, and USB cables fit into USB sockets.Read Full Story