176 citations · 354 across the 5 of their papers we have counts for
5 papers
Accelerating Robot Learning of Contact-Rich Manipulations: A Curriculum Learning Study
Cristian C. Beltran-Hernandez, Damien Petit, Ixchel G. Ramirez-Alpizar +1
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…
Bimanual Shelf Picking Planner Based on Collapse Prediction
T. Motoda, D. Petit, W. Wan +1
In logistics warehouse, since many objects are randomly stacked on shelves, it becomes difficult for a robot to safely extract one of the objects without other objects falling from…
Variable Compliance Control for Robotic Peg-in-Hole Assembly: A Deep Reinforcement Learning Approach
Cristian C. Beltran-Hernandez, Damien Petit, Ixchel G. Ramirez-Alpizar +1
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 extensi…
Team O2AS at the World Robot Summit 2018: An Approach to Robotic Kitting and Assembly Tasks using General Purpose Grippers and Tools
Felix von Drigalski, Chisato Nakashima, Yoshiya Shibata +12
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 gr…
Learning Force Control for Contact-rich Manipulation Tasks with Rigid Position-controlled Robots
Cristian Camilo Beltran-Hernandez, Damien Petit, Ixchel G. Ramirez-Alpizar +4
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…