Predicting human behavior is critical to facilitate safe and efficient human-robot collaboration (HRC). However, human behavior is difficult to predict due to the scarcity of human motion data. This work explores using online adaptation, an online approach, and data augmentation, an offline approach, to deal with the data scarcity challenge in human motion prediction and intention prediction. This paper proposes a novel adaptable human motion prediction framework, RNNIK-MKF, which combines a recurrent neural network (RNN) and inverse kinematics (IK) to predict human motion. A modified Kalman filter (MKF) is applied to robustly adapt the model online to improve the model prediction performance, which is initially learned from scarce training data. In addition, a novel training framework, iterative adversarial data augmentation (IADA), is proposed to learn safe neural network classifiers for intention prediction. The IADA uses data augmentation and expert guidance offline to augment the inadequate data during the training phase and learn robust neural network models from the augmented data. The proposed RNNIK-MKF and IADA are tested on a collected human motion dataset. The experiments demonstrate that our methods can achieve more robust and accurate prediction performance.
Prof. Changliu Liu (Advisor)
Prof. Oliver Kroemer
Prof. Katia Sycara
Zoom Participation. See announcement.