Common Dimensional Autoencoder for Learning Redundant Muscle-Posture 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 agonist-antagonist 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 actuator\u27s 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 actuators\u27 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 degrees-of-freedom complex musculoskeletal robot arm driven by pneumatic artificial muscles.IEEE/RSJ International Conference on Intelligent Robots and Systems (iROS2019), November 4 - 8, 2019, Macau, Chin

Advances in Bio-Inspired Robots

This book covers three major topics, specifically Biomimetic Robot Design, Mechanical System Design from Bio-Inspiration, and Bio-Inspired Analysis on A Mechanical System. The Biomimetic Robot Design part introduces research on flexible jumping robots, snake robots, and small flying robots, while the Mechanical System Design from Bio-Inspiration part introduces Bioinspired Divide-and-Conquer Design Methodology, Modular Cable-Driven Human-Like Robotic Arm andWall-Climbing Robot. Finally, in the Bio-Inspired Analysis on A Mechanical System part, research contents on the control strategy of Surgical Assistant Robot, modeling of Underwater Thruster, and optimization of Humanoid Robot are introduced