Learning Needle Pick-and-Place Without Expert Demonstrations
Learning Needle Pick-and-Place Without Expert Demonstrations
复制标题
无需专家演示即可学习针取放
DOI:
10.1109/lra.2023.3266720
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发表时间:
2023
影响因子:
5.2
通讯作者:
Bendikas R
中科院分区:
文献类型:
--
作者:
Bendikas R
We introduce a novel approach for learning a complex multi-stage needle pick-and-place manipulation task for surgical applications using Reinforcement Learning without expert demonstrations or explicit curriculum. The proposed method is based on a recursive decomposition of the original task into a sequence of sub-tasks with increasing complexity and utilizes an actor-critic algorithm with deterministic policy output. In this work, exploratory bottlenecks have been used by a human expert as convenient boundary points for partitioning complex tasks into simpler subunits. Our method has successfully learnt a policy for the needle pick-and-place task, whereas the state-of-the-art TD3+HER method is unable to achieve success without the help of expert demonstrations. Comparison results show that our method achieves the highest performance with a 91% average success rate.
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DOI:
10.1146/annurev-control-062420-090543
发表时间:
2021-01-01
期刊:
ANNUAL REVIEW OF CONTROL, ROBOTICS, AND AUTONOMOUS SYSTEMS, VOL 4, 2021
影响因子:
--
作者:
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通讯作者:
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影响因子:
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DOI:
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影响因子:
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DOI:
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期刊:
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影响因子:
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DOI:
10.1109/ro-man47096.2020.9223543
发表时间:
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期刊:
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影响因子:
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作者:
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通讯作者:
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