Common Dimensional Autoencoder for Learning Redundant MusclePosture Mappings of Complex Musculoskeletal Robots It has been widely considered that a distinctive feature of musculoskeletal structures is that both the joint angle and stiffness can be changed by exploiting the agonistantagonist driving of the joint. However, musculoskeletal systems in animals and humans are typically highly complex, and the simple agonistantagonist driving is rarely found. Therefore, in accordance with the increasing complexity of musculoskeletal robots, the feature that causes the robot to assume a posture with different stiffness values becomes difficult to achieve, owing to the difficulty in modeling the kinematics. Although datadriven approaches such as the neural network are regarded as suitable for modeling complex relationships, the training data are difficult to obtain because measuring joint stiffness is typically extremely difficult in contrast to measuring an actuatoru0027s state and posture. Hence, we herein propose the common dimensional autoencoder where the encoded feature exhibits identical dimensions to the original input vector. In the proposed network, in parallel with the original unsupervised training using the data of the actuatorsxe2x80x99 states, supervised training at part of the encoded features is performed using posture data. Consequently, features expressing the redundancy of inverse kinematics appear at the remaining part of the encoded features without using data such as joint stiffness. The validity of the proposed method was confirmed successfully through an experiment using a 10 degreesoffreedom complex musculoskeletal robot arm driven by pneumatic artificial muscles.

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.