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- W4285732014 abstract "Imitation learning is a discipline of machine learning primarily concerned with replicating observed behavior of agents known to perform well on a given task, collected in demonstration data sets. In this paper, we set out to introduce a pipeline for collecting demonstrations and training models that can produce motion plans for industrial robots. Object throwing is defined as the motivating use case. Multiple input data modalities are surveyed, and motion capture is selected as the most practicable. Two model architectures operating autoregressively are examined—feedforward and recurrent neural networks. Trained models execute throws on a real robot successfully, and a battery of quantitative evaluation metrics is proposed. Recurrent neural networks outperform feedforward ones in most respects, but this advantage is not universal or conclusive. The data collection, pre-processing and model training aspects of our proposed approach show promise, but further work is required in developing Cartesian motion planning tools before it is applicable in production applications." @default.
- W4285732014 created "2022-07-18" @default.
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- W4285732014 date "2022-07-17" @default.
- W4285732014 modified "2023-10-14" @default.
- W4285732014 title "A Motion Capture and Imitation Learning Based Approach to Robot Control" @default.
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- W4285732014 doi "https://doi.org/10.3390/app12147186" @default.
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