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Musculoskeletal robots that are based on pneumatic actuation have a varietyof properties, such as compliance and back-drivability, that render themparticularly appealing for human-robot collaboration. However, programminginteractive and responsive behaviors for such systems is extremely challengingdue to the nonlinearity and uncertainty inherent to their control. In thispaper, we propose an approach for learning Bayesian Interaction Primitives formusculoskeletal robots given a limited set of example demonstrations. We showthat this approach is capable of real-time state estimation and responsegeneration for interaction with a robot for which no analytical model exists.Human-robot interaction experiments on a 'handshake' task show that theapproach generalizes to new positions, interaction partners, and movementvelocities.

Learning Interactive Behaviors for Musculoskeletal Robots Using Bayesian Interaction Primitives