Trajectory planning by variable length chunk of sequence-to-sequence using hierarchical decoder
Trajectory planning by variable length chunk of sequence-to-sequence using hierarchical decoder
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DOI：
10.1109/amc.2019.8371089
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发表时间：
2018-03
期刊：
影响因子：
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通讯作者：
T. Okamoto;Kyo Kutsuzawa;S. Sakaino;T. Tsuji
中科院分区：
文献类型：
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作者：
T. Okamoto;Kyo Kutsuzawa;S. Sakaino;T. Tsuji
对象操作是机器人支持人类的最重要的问题之一。然而，由于动力学约束，动态操作的轨迹规划是一个困难的问题。本文论述了使用序列到序列（seq2seq）模型的动态操作轨迹规划。在传统方法中，多个样本被分块以减少时间序列的长度。然而，由于块大小是固定的，因此准确度可能降低并且轨迹被截断。因此，我们验证了这些的效果。我们通过使用分层解码器的seq2seq模型解决了这些问题。因此，该模型能够生成比传统方法更精确的轨迹。
Object manipulation is one of the most important issues for robots supporting humans. However, trajectory planning for dynamic manipulation is a difficult issue due to dynamic constraints. This paper deals with trajectory planning for dynamic manipulation using sequence-to-sequence (seq2seq) models. In a conventional method, multiple samples are chunked in order to reduce the length of the time series. However, as the chunk size is fixed, accuracy may be reduced and trajectories are truncated. Therefore, we verified the effects of these. We solved these problems by a seq2seq model using a hierarchical decoder. As a result, proposed model was able to generate a trajectory more accurate than the conventional method.