Learning dexterity
Description
We’ve trained a human-like robot hand to manipulate physical objects with unprecedented dexterity. Our system, called Dactyl, is trained entirely in simulation and transfers its knowledge to reality, adapting to real-world physics using techniques we’ve been working on for the past year. Dactyl learns from scratch using the same general-purpose reinforcement learning algorithm and code as OpenAI Five. Our results show that it’s possible to train agents in simulation and have them solve real-world tasks, without physically-accurate modeling of the world.
Speaker Bio
Wojciech Zaremba is a co-founder of OpenAI (2016-now), where he leads the robotics team, which is working on developing general purpose robots via new approaches to transfer learning and teaching robots unprecedentedly complex behaviors. He received his MS from Warsaw University / Ecole Polytechnique (2013), and his PhD from New York University (2015) under Prof. Rob Fergus and Prof. Yann LeCun. During his PhD, Wojciech spent one year at Facebook AI Research and one year at Google Brain. Wojciech’s PhD and subsequent research contributions have involved using neural network techniques to get computers to learn sophisticated algorithms from raw data: his contributions include the applying translation models to computer programs and building first neural Turing machines with discrete actions. Moreover, he worked on discovery of adversarial examples, improved training of GANs, and development of OpenAI gym. His current work is dedicated towards the development of the General Purpose Robots.