Local Online Motor Babbling: Learning Motor Abundance of a Musculoskeletal Robot Arm * Motor babbling and goal babbling has been used for sensorimotor learning of highly redundant systems in soft robotics. Recent works in goal babbling have demonstrated successful learning of inverse kinematics (IK) on such systems, and suggest that babbling in the goal space better resolves motor redundancy by learning as few yet efficient sensorimotor mappings as possible. However, for musculoskeletal robot systems, motor redundancy can provide useful information to explain muscle activation patterns, thus the term motor abundance. In this work, we introduce some simple heuristics to empirically define the unknown goal space, and learn the IK of a 10 DoF musculoskeletal robot arm using directed goal babbling. We then further propose local online motor babbling guided by Covariance Matrix Adaptation Evolution Strategy (CMAES), which bootstraps on the goal babbling samples for initialization, such that motor abundance can be queried online for any static goal. Our approach leverages the resolving of redundancies and the efficient guided exploration of motor abundance in two stages of learning, allowing both kinematic accuracy and motor variability at the queried goal. The result shows that local online motor babbling guided by CMAES can efficiently explore motor abundance at queried goal positions on a musculoskeletal robot system and gives useful insights in terms of muscle stiffness and synergy.

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.