A recent trend in society is for young parents to ask for support in performing their childcare duties. Against this background and to promote the intermixing of parents and children and support information exchange between parents and nursery teachers, we developed the network-based interactive child-watch system called “Meru-robo Digital Log system”. It is composed of the “Action Switch Platform (AcSP)” (developed by NTT Laboratories) for exchanging dialog with a “personal robot” (called “PaPeRo”) (developed by NEC Corporation) by using cellular phone text messaging. A field trial showed that parents could send their intentions and receive information in a schedule-flexible manner via cellular phone text messaging. Since the robot in the nursery linked the parents to the teacher, their information exchanges did not interfere with the activities of the children. We report results of information exchanges using this system in two situations: parents are the same building (but not the same room as the children) and parents are in other locations going about their daily life. In the limited situation of children playing in a nursery, we found that parents in remote locations could get detailed information about their children’s activities and expressions when desired via asynchronous communication.

We have been studying a remote-collaboration system called SCOPE (sight collaboration by projection effect) featuring image projecting and capturing capabilities as implemented in a maintenance robot. With the help of SCOPE and a remote support person, an on-site worker can perform maintenance very efficiently. We propose a mobile SCOPE that can significantly expand the area of activity of the conventional SCOPE. To enable an on-site worker to share the field of view of a remote support person, we developed a technique for aligning the optical axes of a camera and a projector. We present experimental data that demonstrates the validity of our optomechanical design. Finally, we show that the mobile SCOPE enables workers to continuously share their field of view, no matter where the mobile SCOPE moves

To create a more efficient environment in which to perform remote maintenance, the authors propose a novel collaboration concept, called robot-augmented communication (RAC). In RAC a maintenance robot and an on-site human worker are assisted by another skilled human operator at a remote support center. By emphasizing the intentions of the remote supporter through robot functions that cannot be imitated by humans, RAC can provide effective support for the on-site worker. In the present study, as the first manifestation of RAC, featuring the projection capability of a maintenance robot, a prototype remote-collaboration system named SCOPE (sight collaboration by projection effect) was developed. This system consists of a projector unit with a camera, a multiple control unit (MCU) for video/voice communications, and PCs connected via a network. It has three key functions: creating a shared field of view between a remote supporter and an on-site worker, keeping meta-information related to objects in real space, and projecting meta-information onto the objects. The effectiveness of these functions was evaluated by experiments on two fundamental tasks $"comprehending a target object" and "transmission of instruction details" nvolved in a typical maintenance operation. The experimental results show that each function is effective in shortening the time taken to perform the maintenance operation.

We have developed a highly reliable method of detecting contact-state transitions in real time. In the detection algorithm, feature variables, which fluctuate abruptly when a contact state transition occurs, are selected. The differential of the feature trajectory is monitored to distinguish the transition region from the stable region. To confirm that a state transition has actually occurred in the region, the duration of the region is measured. With this method, a contact-state transition can be detected with a 40-msec delay. We also have developed an experimental manipulation system, in which motion execution management and motion monitoring and execution modules operate independently. Using transputers and a six-degree-of-freedom direct-drive manipulator, a square-peg insertion task is carried out to evaluate the effectiveness of the detection method in an actual environment. The experimental results show that contact-state transitions can be accurately detected and that motion commands can be quickly switched by using the detected transition information.

New methods for teaching and controlling contour-tracking tasks based on contact sensing are developed to improve robotic machining. Accurate contact point detection is introduced to estimating a work contour path with high reliability. These teaching methods reduce the effort for task teaching and improve adaptability to handle changes in task specifications without re-teaching. Real-time contour tracking control using this contact sensing to suppress work uncertainties in process is presented. Experimental results for practical grinding tasks using a 6 DOF manipulator validate our methods.>

This paper discusses a new method for teaching a deburring robot based on demonstration of human skilful motion. The robot is programmed to adjust the tool feedrate in accordance with the varying burr characteristics, such as burr size and material properties. This dynamic change of tool feedrate is motivated by the effective human skill in performing a deburring task. The relationship between the tool feedrate and burr characteristics is obtained from human demonstration data and stored in a computer as an associative memory. This associative memory enables the robot to select the tool feedrate that well matches the burr characteristics. Therefore, the robot motion is always effective in removing burrs and generating smooth finish of workpiece surface without severe tool wear. In order to identify burr characteristics, a laser displacement sensor has been used for direct burr height measurement, and a deburring process model has been applied for material property differentiation. The learned associative memory is stored and represented by a neural network, which can be easily incorporated into robot programming. Experimental results show that a robot can perform a deburring task in a manner similar to its human teacher.>