Výsledky vyhledávání - "Petit, Damien"

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Accelerating Robot Learning of Contact-Rich Manipulations: A Curriculum Learning Study

Autor: Beltran-Hernandez, Cristian C., Petit, Damien, Ramirez-Alpizar, Ixchel G., Harada, Kensuke

The Reinforcement Learning (RL) paradigm has been an essential tool for automating robotic tasks. Despite the advances in RL, it is still not widely adopted in the industry due to the need for an expensive large amount of robot interaction with its e

Externí odkaz: http://arxiv.org/abs/2204.12844

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Variable Compliance Control for Robotic Peg-in-Hole Assembly: A Deep Reinforcement Learning Approach

Autor: Beltran-Hernandez, Cristian C., Petit, Damien, Ramirez-Alpizar, Ixchel G., Harada, Kensuke

Publikováno v: Appl. Sci. 2020, 10(19), 6923

Industrial robot manipulators are playing a more significant role in modern manufacturing industries. Though peg-in-hole assembly is a common industrial task which has been extensively researched, safely solving complex high precision assembly in an

Externí odkaz: http://arxiv.org/abs/2008.10224

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Team O2AS at the World Robot Summit 2018: An Approach to Robotic Kitting and Assembly Tasks using General Purpose Grippers and Tools

Autor: von Drigalski, Felix, Nakashima, Chisato, Shibata, Yoshiya, Konishi, Yoshinori, Triyonoputro, Joshua C., Nie, Kaidi, Petit, Damien, Ueshiba, Toshio, Takase, Ryuichi, Domae, Yukiyasu, Yoshioka, Taku, Ijiri, Yoshihisa, Ramirez-Alpizar, Ixchel G., Wan, Weiwei, Harada, Kensuke

We propose a versatile robotic system for kitting and assembly tasks which uses no jigs or commercial tool changers. Instead of specialized end effectors, it uses its two-finger grippers to grasp and hold tools to perform subtasks such as screwing an

Externí odkaz: http://arxiv.org/abs/2003.02427

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Learning Force Control for Contact-rich Manipulation Tasks with Rigid Position-controlled Robots

Autor: Beltran-Hernandez, Cristian Camilo, Petit, Damien, Ramirez-Alpizar, Ixchel G., Nishi, Takayuki, Kikuchi, Shinichi, Matsubara, Takamitsu, Harada, Kensuke

Reinforcement Learning (RL) methods have been proven successful in solving manipulation tasks autonomously. However, RL is still not widely adopted on real robotic systems because working with real hardware entails additional challenges, especially w

Externí odkaz: http://arxiv.org/abs/2003.00628

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