This paper presents a novel method for controlling a single-joint robot arm driven by two pneumatic artificial muscles (PAMs). We introduce the concepts of the agonist-antagonist muscle-pairs ratio (A-A ratio) and the agonist-antagonist muscle-pairs activity (A-A activity), and demonstrate that our concepts enable separate linear control of the equilibrium joint angle and joint stiffness. We also discuss our approach in comparison with the equilibrium-point (EP) hypothesis.

This paper presents an electromyographic-based human-machine interface for the agonist-antagonist system with two pairs of pneumatic artificial muscles (PAMs) that replicates the human elbow-joint system. We introduce the novel concepts of agonist-antagonist muscle-pair ratio (A-A ratio) and agonist-antagonist muscle-pair activity (A-A activity) to link the human muscle system to the PAM system, and we propose a linear control method translating the equilibrium point of human muscle system into that of PAM system. The human-robot experiment demonstrates the validity of the proposed method.

The need of solving the problem of multi-degrees of-freedom in motor control is increasing as human-like robots become more popular these days. Muscle synergies hypothesis is a prominent suggestion for this problem. In this paper, we investigated the muscle synergies extracted from human subjects in a producing-force task to explore the mechanism of the human's skillful hand-movement at producing force while manipulating objects. We observed a high correlation estimation of the muscle synergies with the hand force, and derived a role division of muscle synergies in generating the hand force as well. These results give an optimal but simple way to control robots so as to make the robots' movement as skillful as human's. To verify this idea, we proposed "synergy based control" method using the characteristics of human's muscle synergies observed, and tested it on a pneumatic-driven arm robot whose structure assumes the human arm's. The program is in its first stage to control the robot.

The study of decomposition of body movement into motor primitives is evolving in neuroscience. Meanwhile, in robotics, the motor control of human-like musculoskeletal robots is difficult due to redundant degrees of freedom (DOF) of the robot body. The application of the concept of decomposing into units of motor function to robotics is anticipated to render the control of the robot low-dimensional. We try to achieve fewer-DOF control of a human-like musculoskeletal robot by using our knowledge of the units of motor function based on muscular coordination. In this paper, we introduce “the agonist-antagonist muscle pairs (A-A) ratio” and “A-A activity.” These parameters are defined by using electromyographic (EMG) data for describing the coordination between the agonist and antagonist muscles. Human running is decomposed into two units of motor function using Principal Component Analysis (PCA) for these parameters. We propose a new method of modular control of a musculoskeletal leg robot using the extracted patterns of muscle coordination, and we thus find kinematic meanings of the patterns of muscle coordination.

This paper focuses on dynamical role divisions of cooperation interaction between two human subjects in a typical cooperation task, the crank-rotation task. The dynamical role division in which each subject plays a specialized role without conscious understanding is a key issue that brings the efficiency to the performance of the crank-cooperation work. By investigating kinetics and muscle activities, we found out interesting results about the correlation between the muscle activities and the hand motion, thus, suggesting a method to design control of robots which involve in the crank-cooperation task and presumably in other cooperative interaction with humans.

This paper discusses skillful role divisions of coordinated motion between two agents in a crank-rotation task. The roles for coordination, called ¿dynamical role division,¿ emerge from dynamic interaction between the agents, through which each agent comes to play a specialized role without conscious understanding. This paper also proposes a novel approach to apply this latent skill in coordinated motions to human-robot coordination, and showing the following advantages of this method: 1) controls of each agent's actions are simplified; 2) task performances are improved in a simple manner.