Learning Interactive Behaviors for Musculoskeletal Robots Using Bayesian Interaction Primitives
Joseph Campbell, Arne Hitzmann, Simon Stepputtis, Shuhei Ikemoto, Koh Hosoda, Heni Ben Amor
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2019
Conference Paper

nMusculoskeletal robots that are based on pneumatic actuation have a variety of properties, such as compliance and back-drivability, that render them particularly appealing for human-robot 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 real-time state estimation and response generation for interaction with a robot for which no analytical model exists. Human-robot interaction experiments on a ‘handshake’ task show that the approach generalizes to new positions, interaction partners, and movement velocities.

title={Learning Interactive Behaviors for Musculoskeletal Robots Using Bayesian Interaction Primitives},
author={Joseph Campbell and Arne Hitzmann and Simon Stepputtis and Shuhei Ikemoto and Koh Hosoda and Heni Ben Amor},