Continuous control actions learning and adaptation for robotic manipulation through reinforcement learning

This paper presents a learning-based method that uses simulation data to learn an object manipulation task using two model-free reinforcement learning (RL) algorithms. The learning performance is compared across on-policy and off-policy algorithms: Proximal Policy Optimization (PPO) and Soft Actor-C...

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Veröffentlicht in:	Autonomous robots 2022-03, Vol.46 (3), p.483-498
Hauptverfasser:	Shahid, Asad Ali, Piga, Dario, Braghin, Francesco, Roveda, Loris
Format:	Artikel
Sprache:	eng
Schlagworte:	Adaptation Algorithms Artificial Intelligence Computer Imaging Control Engineering Machine learning Mechatronics Optimization Pattern Recognition and Graphics Robotics Robotics and Automation Robots Vision
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