Robust Motion Mapping Between Human and Humanoids Using CycleAutoencoder - Stanley, Matthew; Tao, Lingfeng; Zhang, Xiaoli (2021 IEEE International Conference on Robotics and Biomimetics (ROBIO), 2021)
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.