Reactive Interaction Through Body Motion and the PhaseStateMachine Between humans, body motion is an intuitive and ubiquitous means to coordinate interactions, but so far collaborative robots have not embraced this mode of communication. One reason for this is that conventional behavior generation systems use finite state machines which make it exceptionally difficult to ingest and produce the inherently continuous and concurrent information flow that body motion provides. We propose a new, reactive motion generator based on a dynamical system instead. It mimics a conventional state machine except that transitions are not instantaneous but timeextended, reversible, and have phases. Moreover, consecutive states and transitions may overlap during execution. Most notably, more than one transition can be active at the same time if they share predecessor state. Together, these unique features enable instantaneous and gradual responses to novel information and continuous decision processes without changing the state graph itself. We demonstrate the systemxe2x80x99s capabilities in an object handover task coordinated by body motion.

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.