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Found 4 papers, 2 papers with code
Accelerating Robot Learning of Contact-Rich Manipulations: A Curriculum Learning Study
The Reinforcement Learning (RL) paradigm has been an essential tool for automating robotic tasks.
Variable Compliance Control for Robotic Peg-in-Hole Assembly: A Deep Reinforcement Learning Approach
Industrial robot manipulators are playing a more significant role in modern manufacturing industries.
Team O2AS at the World Robot Summit 2018: An Approach to Robotic Kitting and Assembly Tasks using General Purpose Grippers and Tools
We propose a versatile robotic system for kitting and assembly tasks which uses no jigs or commercial tool changers.
Learning Force Control for Contact-rich Manipulation Tasks with Rigid Position-controlled Robots
Thirdly, we developed a fail-safe mechanism for safely training an RL agent on manipulation tasks using a real rigid robot manipulator.
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