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- W4386750828 abstract "In this paper, we propose a deep reinforcement learning-based framework that enables adaptive and continuous control of a robot to push unseen objects from random positions to the target position. Our approach takes into account contact information in the design of the reward function, resulting in improved success rates, generalization for unseen objects, and task efficiency compared to policies that do not consider contact information. Through reinforcement learning using only one object in simulation, we obtain a learned policy for manipulating a single object, which demonstrates good generalization when applied to the task of pushing unseen objects. Finally, we validate the effectiveness of our approach in real-world scenarios." @default.
- W4386750828 created "2023-09-15" @default.
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- W4386750828 date "2023-09-14" @default.
- W4386750828 modified "2023-10-17" @default.
- W4386750828 title "Learning adaptive reaching and pushing skills using contact information" @default.
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- W4386750828 doi "https://doi.org/10.3389/fnbot.2023.1271607" @default.
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