We investigated the effect of height of a robot on comfortableness of spoken dialog with the robot. We created a robot that could change the height continuously, and carried out dialog experiment with 18 subjects changing the robot's height. From the experimental result revealed the two observations: the "comfortable height" of a robot was lower than the eye height of a subject, and the comfortableness of dialog reduced when the height of the robot changed by 200 mm from the comfortable height.

This paper proposes a computational model for the cerebellum and spinal code. The proposed motion control scheme makes it possible to stabilize the posture of hand to the desired equilibrium trajectories. In this model, a motor neuron regulate the rest length of muscle to compensate the changes of equilibrium joint positions caused by the external load and the cerebellum compensates the inertial forces to stabilize the equilibrium position of joints. The asymptotic stability of joint angles to the desired equilibrium joint positions is proven theoretically and the performance of the control model is verified by some simple computer simulations.

Nonlinear H/sub /spl infin// optimal control theory has been well researched in previous years. However, proposed control schemes are quite complicated and hardly feasible in construction of a controller actually for robotic systems. As for robot control, it has been shown that the passivity concept plays a key role in constructing good controllers for dexterous motion with or without tool endpoint constraint. In this paper, the authors propose an H/sub /spl infin//-optimal tuning method for these controllers established on the passivity-based concept. By using the passivity concept, the conditions for H/sub /spl infin//-optimal tuning can be obtained in a simple and clear form. As an application of this H/sub /spl infin// control scheme, it is shown that, even for a trajectory tracking problem for a robot under geometrical endpoint constraint, model-based hybrid adaptive control methods previously proposed can be extended to cope with H/sub /spl infin// tuning in the sense of disturbance attenuation.

A learning controller for coordination of multiple manipulators holding an object rigidly is proposed based on the principle of joint-space orthogonalization. It is devised by referring to (1) the fact that dynamics of multiple manipulators resembles that of a single robot under geometric endpoint constraint except the term of the sum of forces exerted from manipulators to the object, (2) the passivity of the dynamics of multiple manipulators, (3) the use of feedback signals of velocity error and force error which are projected to their respective manifolds to be orthogonal to each other in joint-space, and (4) the passivity of the total error system. The convergence of tracking position and force errors is proved theoretically provided that multiple manipulators hold the object rigidly and are initialized at the same posture at the beginning of each learning trial. The effectiveness of this controller is ascertained by computer simulation by using two manipulators with three joints.