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Found 4 papers, 1 papers with code
Sim-to-Real Deep Reinforcement Learning with Manipulators for Pick-and-place
This paper proposes a self-supervised vision-based DRL method that allows robots to pick and place objects effectively and efficiently when directly transferring a training model from simulation to the real world.
Sim-and-Real Reinforcement Learning for Manipulation: A Consensus-based Approach
Sim-and-real training is a promising alternative to sim-to-real training for robot manipulations.
Accelerated Sim-to-Real Deep Reinforcement Learning: Learning Collision Avoidance from Human Player
An efficient training strategy is proposed to allow a robot to learn from both human experience data and self-exploratory data.
3D Vision-guided Pick-and-Place Using Kuka LBR iiwa Robot
This paper presents the development of a control system for vision-guided pick-and-place tasks using a robot arm equipped with a 3D camera.
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