This paper presents adaptive tracking control with a partial regressor for multi-joint robots. The proposed controller can abbreviate the elaborate computations of a regressor matrix, which needs to be wholly calculated in conventional adaptive control. Therefore, this controller can be easily implemented in robotic systems. Even though the proposed controller does not utilize robustification techniques of adaptive control such as deadzone, σ-modification, e-modification, and projection, the trajectory tracking performance and robustness were guaranteed through analysis of the time-averaged tracking error.

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.

This paper describes a hierarchical architecture for rhythmic coordination between robots, which suits a juggling-like ball-passing task involving sensory-motor coordination. The proposed architecture contains two passive-control mechanisms, which include (1) open-loop stability of mechano-physical system and (2) entrainment between mechano-physical system and neuron-like pattern generating system. We demonstrate a stable motion pattern between robots and emphasize that these passive-control mechanisms lead to the emergence of dynamic coordination for the successful task

This paper presents a new ankle-foot orthosis (AFO) for hemiplegic patients. The proposed AFO has a pneumatic passive element with a rotation axis at the ankle joint. The ankle-joint constraint force can be adjusted according to the subject's gait phase. We confirmed that adjusting of the pneumatic pressure in the proposed pneumatic passive element enables the device to duplicate the performance of many conventional AFOs

This paper describes the hierarchical architecture for a rhythmic coordination between robots, which suits juggling-like tasks involving sensory-motor coordination. The authors' approach, which is interpreted as a "bidirectional weak coupling" to the environment, does not require either the environmental model or continuously monitoring the environment but can adapt the robots to a change in the environment, owing to the interaction between the robots and the environment at the ball contact. The proposed architecture contains two passive-control mechanisms, the "open-loop stable mechanism" and the "entrainment mechanism," that lead to the emergence of self-organized temporal structure for rhythmic movement. This dynamic pattern in the whole system realizes the stable coordinated motion between robots. The authors demonstrate two motion patterns between two robots passing two balls, and confirm the effectiveness of the approach.

This paper describes the properties of a set of simple neural network oscillators suited to two robotic tasks. One robotic task is "wall-bouncing", in which the robot repeats the process of hitting balls that rebound from the wall. Another robotic task is "passing a ball", in which two robots repeat the process of passing balls to each other. The motions of the robot (paddle) are controlled by a set of neural oscillators consisting of four weakly coupled Bonhoffer-van der Pol (BVP) oscillators. We demonstrate that rhythmic movement of the paddle emerges as a stable limit cycle generated by the global entertainment between the paddle, the neural system, and the environment, including balls.

In order for a robotic system autonomously to capture an unknown rigid body rotating freely in zero gravity, it is necessary for it to detect the object's motion. This paper presents a method for estimating object motion based on position information concerning a point located on the object's surface. We assume that the object's motion is described by the superposition of the rotations with constant angular velocities. When position sensors, etc., can obtain three dimensional position information about a point on the object, we can estimate parameters describing its motion. In other words, using this method we can calculate each frequency and direction of the axes of rotation by applying a Fourier transform to a time sequence of the point's three dimensional position.