Robust Motion Mapping Between Human and Humanoids Using CycleAutoencoder
Robust Motion Mapping Between Human and Humanoids Using CycleAutoencoder
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DOI：
10.1109/robio54168.2021.9739345
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发表时间：
2021-12
期刊：
影响因子：
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通讯作者：
Matthew Stanley;Lingfeng Tao;Xiaoli Zhang
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文献类型：
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作者：
Matthew Stanley;Lingfeng Tao;Xiaoli Zhang
遥操作需要精确、鲁棒的人与类人运动之间的运动映射，以产生直观的类人运动机器人控制。数据驱动的方法经常被部署，因为它可以产生直观的、实时的运动映射。在使用这些方法时，共同的焦点是运动映射模型的准确性。然而，需要努力使映射模型在面对噪声或不完整数据集时具有鲁棒性。换句话说，模型需要学习可泛化的映射规则，而不仅仅是准确地预测训练数据。为了创建一个鲁棒和精确的运动映射模型，我们开发了新的循环自编码器方法。该方法使用传统损耗、混合损耗和周期损耗同时训练两个自编码器。这些损失使自编码器能够重建人与类人之间的相互运动。与训练传统的自编码器相比，这使得该方法能够以更高的精度和鲁棒性学习映射。人体受试者参与实验结果表明，与其他基于自编码器的映射方法相比，CycleAutoencoder方法具有较好的映射精度和鲁棒性。
Teleoperation needs accurate and robust motion mapping between human and humanoid motion to generate intuitive robot control with human-like motion. Data-driven methods are often deployed as it can result in intuitive, real time motion mapping. When using these methods, the common focus is on the accuracy of the motion mapping model. However, effort needs to be put into making the mapping model robust in face of noisy or incomplete dataset. In other words, the model needs to learn the generalizable mapping rules, not just be accurate in predicting the training data. To create a robust and accurate model for motion mapping, we developed the novel CycleAutoencoder method. This method simultaneously trains two autoencoders using traditional losses, mixed losses, and cycle losses. These losses allow the autoencoders to reconstruct the motion mutually between humans and humanoids. This allows the method to learn the mapping with improved accuracy and robustness compared to training a traditional autoencoder. The results of human subject involved experiments demonstrated that the CycleAutoencoder method can achieve both accuracy and robustness for the mapping compared with other autoencoder-based mapping methods.