In this paper, we develop a novel adaptive motion imitation algorithm (AMI) for robotic systems. Although AMI can be used in a variety of human-robot interaction scenarios, we are particularly interested in robotic rehabilitation where the robot plays the role of demonstrating and practicing challenging motion physiotherapy. During therapy, the robot first demonstrates a reference trajectory to the patient that needs to be repeated during practice and then adapts its motion to a cyclic speed and amplitude based on the patient's abilities. Using this algorithm, the robotic system learns an upper-body motion of the human user and performs a unique, similar, and easier motion based on the learned trajectory from the user. Adaptation in the AMI is based on deep reinforcement learning with deep deterministic policy gradient implemented in the Robot Operating System (ROS) environment. Experimental data collected from 11 users during upper body human-robot imitation sessions with social robot Zeno was used to show that the algorithm can learn reference elbow joint trajectories of the user in an off-line manner after just a few cycles. Finally, we also implemented the algorithm online using the Baxter robot to demonstrate its learning and playback performance.

In this paper, we propose a novel online algorithm for motion similarity measurements during human-robot interaction (HRI). Specifically, we formulate a Segment-based Online Dynamic Time Warping (SODTW) algorithm that can be used for understanding of repeated and cyclic human motions, in the context of rehabilitation or social interaction. The algorithm can estimate both the human-robot motion similarity and the time delay to initiate motion and combine these values as a metric to adaptively select appropriate robot imitation repertoires. We validated the algorithm offline by post-processing experimental data collected from a cohort of 55 subjects during imitation episodes with our social robot Zeno. Furthermore, we implemented the algorithm online on Zeno and collected further experimental results with 13 human subjects. These results show that the algorithm can reveal important features of human movement including the quality of motion and human reaction time to robot stimuli. Moreover, the robot can adapt to appropriate human motion speeds based on similarity measurements calculated using this algorithm, enabling future adaptive rehabilitation interventions for conditions such as Autism Spectrum Disorders (ASD).