Learning dexterous in-hand manipulation

Medientyp: E-Artikel
Titel: Learning dexterous in-hand manipulation
Beteiligte: Andrychowicz, OpenAI: Marcin; Baker, Bowen; Chociej, Maciek; Józefowicz, Rafal; McGrew, Bob; Pachocki, Jakub; Petron, Arthur; Plappert, Matthias; Powell, Glenn; Ray, Alex; Schneider, Jonas; Sidor, Szymon; Tobin, Josh; Welinder, Peter; Weng, Lilian; Zaremba, Wojciech
Erschienen: SAGE Publications, 2020
Erschienen in: The International Journal of Robotics Research
Sprache: Englisch
DOI: 10.1177/0278364919887447
ISSN: 0278-3649; 1741-3176
Schlagwörter: Applied Mathematics ; Artificial Intelligence ; Electrical and Electronic Engineering ; Mechanical Engineering ; Modeling and Simulation ; Software
Entstehung:
Anmerkungen:
Beschreibung: <jats:p> We use reinforcement learning (RL) to learn dexterous in-hand manipulation policies that can perform vision-based object reorientation on a physical Shadow Dexterous Hand. The training is performed in a simulated environment in which we randomize many of the physical properties of the system such as friction coefficients and an object’s appearance. Our policies transfer to the physical robot despite being trained entirely in simulation. Our method does not rely on any human demonstrations, but many behaviors found in human manipulation emerge naturally, including finger gaiting, multi-finger coordination, and the controlled use of gravity. Our results were obtained using the same distributed RL system that was used to train OpenAI Five. We also include a video of our results: https://youtu.be/jwSbzNHGflM . </jats:p>

Nur in Feld suchen:

Zuletzt gesuchte Begriffe: