In this paper, we introduce the health exercise robot TAIZO, and the social background of its development. TAIZO can be controlled through both vocal commands and key input. Being roughly humanoid in shape it can demonstrate physical exercises in collaboration with the human demonstrator. Through the experiment, we measure both the usability of keypad and vocal commands, and also their demonstrative effects. Through real demonstration, we confirmed that the robot is effective in drawing in onlookers and encourage them to engage in health exercises.

A method of detecting and tracking moving human speech events by integrating audio and video signals using a particle filter is proposed and evaluated. Using the particle filter, location, on/off status, and human/non-human determination can be estimated simultaneously for multiple sound sources. Experiments demonstrate that the proposed method performs well for the data obtained in an ordinary meeting room using a microphone array and a monocular camera.

For cooperative work of robots and humans in the real world, a communicative function based on speech is indispensable for robots. To realize such a function in a noisy real environment, it is essential that robots be able to extract target speech spoken by humans from a mixture of sounds by their own resources. We have developed a method of detecting and extracting speech events based on the fusion of audio and video information. In this method, audio information (sound localization using a microphone array) and video information (human tracking using a camera) are fused by a Bayesian network to enable the detection of speech events. The information of detected speech events is then utilized in sound separation using adaptive beam forming. In this paper, some basic investigations for applying the above system to the humanoid robot HRP-2 are reported. Input devices, namely a microphone array and a camera, were mounted on the head of HRP-2, and acoustic characteristics for sound localization/separation performance were investigated. Also, the human tracking system was improved so that it can be used in a dynamic situation. Finally, overall performance of the system was tested via off-line experiments.

We propose a method for extracting low-dimensional features from omnidirectional images to be used for robot localization and navigation. Edge detection is combined with thresholding to locate sharp edge pixels, the coordinates of which are fed into a Parzen density estimator (1962) to compute the edge spatial density. The use of the fast Fourier transform makes this density estimate feasible in real-time, while principal component analysis further drops the dimensionality of the resulting feature vector to a manageable number. We show experimental results from a Nomad XR4000 robot in an office environment.

We have been developing an autonomous robotic agent that helps people in a real world environment, such as in an office. When a robotic agent works by cooperating with a person in a real world environment, it must manage a lot of information and deal with the knowledge and languages that people usually use. Therefore it is important for the agent to recognize what people request as soon as possible. To realize common communication with people, the agent should provide robust communicative functions to obtain information from people. We discuss communicative functions of our robotic agent called Jijo-2. Especially we focus on the problem of detecting human faces, and discuss how a method of detecting a human face can be robustly archived.

This paper proposes a multi-agent software architecture for controlling higher level behaviors of a mobile office-conversant robot. In order to keep the whole system comprehensive and extensible, a group structure is introduced into multi-agent architecture. In the architecture, the whole system is divided into several behavior-manager agents and the behavior-manager manages multiple behavior agents, which are dynamically created and destroyed on demands. Several behaviors and behavior-managers are implemented as classes of an object-oriented Lisp. Dialog-based map learning and teleoperation experiments with real mobile robot Jijo-2 are shown for evaluating the architecture.

In order for our office conversant robot to take robust actions according to events rising from different kind of sensors, a layered process network architecture based on an event-driven control model is presented. In this architecture, high level modules are freed from time consuming checking of sensors, and are able to follow multiple scenario-driven consistent behaviors, allowing interruption from human and environment. Programs are written as series of event-action pairs, which are also effective to keep reactiveness and modularity high.

A design of mobile robot for robust life-long navigation in office environment is proposed and evaluated. The key idea is combining probabilistic map and dialog with humans for reducing the location uncertainty. Bayesian inference with the map represented by probabilistic automata is used in order to reduce the number of queries and to evaluate the success rate of planned paths. We experimentally implemented the design using a simple Bayesian network with continuous nodes and demonstrated its effectiveness in a real environment.

In an environment with plural robots, an assembly plan may be changed according to the constraints or abilities of the manipulator which executes the task. For an assembly task composed of subassemblies which can be executed at the same time, the assembly plan depends on the constraints of the manipulator. This paper describes an approach to an assembly planning system using contract net protocols. The contract nets are applied between part agents and robot agents which have a little automatic faculty. An assembly plan is generated based on searching an AND/OR graph for a given product using heuristic estimation.

A way of parameterizing a spherical obstacle in the configuration space (C-Space) and the properties of a moving spherical obstacle are described. A spherical obstacle in a work space (W-Space) is transformed into a characteristic region in C-Space, and can be approximated as a set of geometric objects. The transformation of a complex work space into C-Space is described in terms of unifying the transformation of each element of a set of spheres. A collision-free path is made by using a penalty-function-method. for a moving spherical obstacle in the work space, the penalty-function is modified according to the factors of the velocity along axes in polar coordinates system. Simulation results are presented for the case of two manipulators in W-Space.>