Fumio Kanehiro

Vision-based adaptive and interactive behaviors in mechanical animals using the remote-brained approach

We present a variety of vision-based adaptive and interactive behaviors in mechanical animals. The mechanical animal is a multi-legged robot designed as a remote-brained robots which does not bring its own brain within the body. It leaves the brain in the mother environment and talks with it by radio links. The brain is raised in the mother environment inherited over generations. The key idea of the remote-brained approach is that of interfacing intelligent software systems with real robot bodies through wireless technology. In this framework the robot system can have a powerful vision system in the brain environment. We have applied this approach toward formation of vision-based dynamic and intelligent behaviors of mechanical animals such as doglike robots and apelike robots. In this paper we introduce the remote-brained approach and describe some remote-brained robots and visual processes for adaptive and interactive behaviors with them. >

Whole body teleoperation of a humanoid robot - a method of integrating operator's intention and robot's autonomy

This paper proposes a new method for the teleoperation of humanoid robots which integrates human operators intention with robots autonomy. Getting hints from human conscious and subconscious motion generations, we propose a method which generates whole body motions of a humanoid robot satisfying operators desired movement of specific points on which the operator focuses as well as the robots balance. The proposed method is based on a whole body momentum control. We also introduce a function of automatic adjustments of the position of the center of mass and torso orientation in order to expand the reachable area of a humanoid robot. The effectiveness of the method is confirmed by using humanoid robot simulator OpenHRP with physical parameters of real humanoid robot HRP-1S.

Development of a remote-brained humanoid for research on whole body action

In this paper, a second generation remote-brained humanoid robot is presented which is developed for research on whole body action. Humanoid robots are important as a platform for the integration of techniques and algorithms acquired from research on manipulators, legged robots and so on. And they have new problems such as how to acquire, memorize, select and carry out various motions which use their whole bodies efficiently. In order to do research on these problems, a good robot body, which has whole body and enough performance for walking and getting up when it falls down is necessary. At the same time, a powerful brain is necessary which can be evolved through the body. As a solution to these demands, remote-brained approach was proposed and several humanoid robots was developed. Using these robots, several researches were done. For example, a brain framework called BeNet, an action acquisition using GA and NN and so on. They were done using a simple wireless connection. Interface between robot brain and its body to concentrate on a high-level problem. However this interface limited actions the robot can do simultaneously. In this paper, this old interface is taken to the next step. New interface has multiple actuator control methods which are switched on demand, and an onbody microprocessor network which controls actuators, measures sensors and interacts with a brain. Finally a new humanoid robot is developed on this interface.

Vision-equipped apelike robot based on the remote-brained approach

Presents a new type of robot which has two arms and two legs like an ape and is aimed at studying a variety of vision-based behaviors. The robot does not bring its own brain within the body. It leaves the brain in the mother environment and talks with it by radio links. The brain is raised in the mother environment inherited over generations. The key idea of the remote-brained approach is that of interfacing intelligent software systems with real robot bodies through wireless technology. In this framework the robot system can have a powerful vision system in the brain environment. The authors have applied this approach to formation of vision-based dynamic and intelligent behaviors of a multi-limbed mobile robot. In this paper the authors present an apelike robot with the remote-brained environment and describe vision-based experiments carried out with the apelike robot.

Action acquisition framework for humanoid robots based on kinematics and dynamics adaptation

It is not simple for humanoid to acquire its actions automatically because: 1) it has very wide search space due to many DOF and is not fixed on the ground; 2) complex actions are difficult to describe using indirect evaluation functions; and 3) a humanoid is required to behave naturally like a human does. Kinematics adaptation generates an action outline which is a rough action description from teachers action (motion capture data) by absorbing a difference of structures between the human and robot. Then a target action is acquired through dynamics adaptation by searching an action which is executable dynamically. Advantages of this framework are that: 1) the target action is described in an intuitive representation, 2) the search space is reduced in the neighborhood space, and 3) the acquired action will be a natural motion.

Remote-brained ape-like robot to study full-body mobile behaviors based on simulated models and real-time vision

We present a new type of robot which has two arms and two legs like an ape and is aimed to study a variety of behaviors based on models and vision. The robot is designed as a remote-brained robot w...