Learning Interactive Behaviors for Musculoskeletal Robots Using Bayesian Interaction Primitives Musculoskeletal robots that are based on pneumatic actuation have a variety of properties, such as compliance and backdrivability, that render them particularly appealing for humanrobot collaboration. However, programming interactive and responsive behaviors for such systems is extremely challenging due to the nonlinearity and uncertainty inherent to their control. In this paper, we propose an approach for learning Bayesian Interaction Primitives for musculoskeletal robots given a limited set of example demonstrations. We show that this approach is capable of realtime state estimation and response generation for interaction with a robot for which no analytical model exists. Humanrobot interaction experiments on a xe2x80x99handshakexe2x80x99 task show that the approach generalizes to new positions, interaction partners, and movement velocities.

Human Intention Inference and OnLine Human Hand Motion Prediction for HumanRobot Collaboration With recent development of robotic technology, it is increasingly common that robot coexist with human, in which humans and robots share a common workspace and work in close proximity. To maintain efficiency and ensure safety under these circumstances, robot should have the ability to predict the future human motion based on the observed ongoing motion. In this paper, we present a methodology for online inference of human intention and prediction of human hand motion. The proposed framework is built using Probabilistic Dynamic Movement Primitive (PDMP). In the offline stage, a set of PDMPs is constructed based on the recorded demonstrations and they will then be used for inferring human intention and predicting human hand motion in the online stage. A proof of concept evaluation is carried out in a tabletop manipulation task. Experimental result shows the proposed framework achieve high performance in human intention inference and in the trajectory similarity between the predicted and the actual hand movement under the normally defined environment. We also show the proposed framework can adapt and generalize to the newly defined environment.