Monte Carlo localization (MCL) is a sample-based approach for representing probability density for the pose of a robot. MCL is widely used for mobile robots because of its robustness with respect to sensor noise. On the other hand, MCL can fail to estimate a pose of a robot if objects block measuring range of a laser sensor of the robot. Even though MCL fails to estimate the pose of the robot, MCL does not stop to estimate the pose because MCL does not have a self-diagnostic function. Therefore, detection of localization failure is essential for a mobile robot application. In the present paper, we propose a novel approach that detects the localization failure of MCL through logistic regression. The proposed approach has two advantages. First, it can detect whether position errors is larger than 0.15 m with a high accuracy. Second, the proposed method can detect localization failure by means of a statistically modeled equation. Moreover, as an example of an application using the probability of localization failure, we have proposed a hybrid localization scheme with MCL and laser odometry. The practical effectiveness of the proposed scheme is verified through experiments.

Human-interactive robots, such as those used for nursing, which share humans' environments and interact with them, should be covered with soft areal tactile sensors for safety and dexterous manipulation. We report the successful development of the tactile sensor system of our human-interactive robot named RI-MAN, which can lift up a dummy human.

Recently, active researches have been performed to increase a robot's intelligence so as to realize the dexterous tasks in complex environment such as in the street or homes. However, since the skillful human-like task ability is so difficult to be formulated for the robot, not only the analytical and theoretical control researches but also the direct human motion mimetic approach is necessary. In this paper, we propose that to realize the environmental interactive tasks, such as human care tasks, it is insufficient to replay the human motion along. We show a novel motion generation approach to integrate the cognitive information into the mimic of human motions so as to realize the final complex task by the robot.

Our goal is to create advanced engineering systems such as a soft human interactive robot. The robot developed here is named RI-MAN. RI-MAN exhibits the skill and ability to realize human care and welfare tasks. RI-MAN can search out a specific person in real time by fuing audio and visual information, and understand human speech based on a sound recognition function. In addition, RI-MAN's body is coverd with soft touch sensors, and RI-MAN can react to the amplitude and location of external forces. Using all these sensor functions, RI-MAN can successfully follow human commands and hold up a dummy of the same size as an adult human. RI-MAN will become an invaluable partner robot.

Constrained human movements are considered in this paper. The external constraints decrease the mobility of the human arm and lead to the redundancy in the distribution of the interaction force between the arm joints. To investigate the trajectory formation in the constrained human movements, we first develop a novel experimental system with interchangeable geometric constraints. Then, we examine the trajectory of human arm for an elliptic constraint. To clarify the trajectory formation in constrained point-to-point motions, we analyze experimental data and test them against predictions obtained by conventional criteria of optimality. It is found in the comparative analysis that the best prediction is given by the minimum muscle force change criterion.

With the development of information science and robotic technology, it becomes more important to generate human interactive robots. The design platform for developing such robots should satisfy three basic conditions: (1) it can test safely the performance of the robot through the physical interaction with human, (2) human subject can estimate subjectively the outside appearance of the robot, and (3) it can simulate the dynamic human interactive robot motion within real-time. This paper proposes our immersion type dynamic simulation platform. An application to estimate the robots performance when performing cooperative object lifting task with human subject is chosen in order to show the effectiveness of our system. The analysis of the recorded data is useful to design the novel human interactive robots.