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• W3195643803 abstract "Simulation to real (Sim-to-Real) is an attractive approach to construct controllers for robotic tasks that are easier to simulate than to analytically solve. Working Sim-to-Real solutions have been demonstrated for tasks with a clear single objective such as reach the target. Real world applications, however, often consist of multiple simultaneous objectives such as reach the target but avoid obstacles. A straightforward solution in the context of reinforcement learning (RL) is to combine multiple objectives into a multi-term reward function and train a single monolithic controller. Recently, a hybrid solution based on pre-trained single objective controllers and a switching rule between them was proposed. In this work, we compare these two approaches in the multi-objective setting of a robot manipulator to reach a target while avoiding an obstacle. Our findings show that the training of a hybrid controller is easier and obtains a better success-failure trade-off than a monolithic controller. The controllers trained in simulator were verified by a real set-up." @default.
• W3195643803 created "2021-08-30" @default.
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• W3195643803 date "2021-09-27" @default.
• W3195643803 modified "2023-10-11" @default.
• W3195643803 title "Monolithic vs. hybrid controller for multi-objective Sim-to-Real learning" @default.
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• W3195643803 doi "https://doi.org/10.1109/iros51168.2021.9636426" @default.
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